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Briefings in Bioinformatics, Volume 24
Volume 24, Number 1, January 2023
- Jiung-Wen Chen
, Lisa Shrestha
The hitchhikers' guide to RNA sequencing and functional analysis. - Jiacheng Lin, Lijun Wu
R2-DDI: relation-aware feature refinement for drug-drug interaction prediction. - Jing Wang
scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network. - Eva María Trinidad
Liquidhope: methylome and genomic profiling from very limited quantities of plasma-derived DNA. - Correction to: siGCD: a web server to explore survival interaction of genes, cells and drugs in human cancers.
- Yue-Hua Feng
A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions. - Xiayang Li, Moxuan Chen, Huaming Wu:
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage. - Chengqian Lu
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network. - Ziyang Tang
spaCI: deciphering spatial cellular communications through adaptive graph model. - Bailing Zhou
EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning. - Yimeng Wang, Mengting Huang, Hua Deng, Weihua Li, Zengrui Wu
Identification of vital chemical information via visualization of graph neural networks. - Ilias Moutsopoulos, Eleanor C. Williams
bulkAnalyseR: an accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data. - Yuanyuan Chen
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings. - Ryley Dorney
Recent advances in cancer fusion transcript detection. - Lei Wang, Shao-Hua Shi
Reducing false positive rate of docking-based virtual screening by active learning. - Khandakar Tanvir Ahmed, Sze Cheng, Qian Li
Incomplete time-series gene expression in integrative study for islet autoimmunity prediction. - Denis Beslic
Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly. - Jun Liu, Kai-Long Zhao, Guijun Zhang
Improved model quality assessment using sequence and structural information by enhanced deep neural networks. - Wenjie Du, Xiaoting Yang, Di Wu, Fenfen Ma, Baicheng Zhang
Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers. - Jiayin Zhou
To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities. - Wayland Yeung, Zhongliang Zhou
Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies. - Wayland Yeung, Zhongliang Zhou
Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings. - Vandana Bharti, Shabari S. Nair, Akshat Jain, Kaushal Kumar Shukla, Bhaskar Biswas:
Concept drift detection in toxicology datasets using discriminative subgraph-based drift detector. - Fang Ge, Chen Li
VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants. - Kai Yu
dSCOPE: a software to detect sequences critical for liquid-liquid phase separation. - Xing Chen, Li Huang:
Computational model for disease research. - Yanan Tian, Xiaorui Wang
Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism. - Danni Hong, Hongli Lin, Lifang Liu, Muya Shu, Jianwu Dai, Falong Lu
Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis. - Lishen Zhang, Chengqian Lu
CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features. - Zheng Zhang
RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. - Sayed-Rzgar Hosseini
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy. - Kai Zheng
SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs. - Wenchuang He
Master graph: an essential integrated assembly model for the plant mitogenome based on a graph-based framework. - Nishant Kumar, Sumeet Patiyal, Shubham Choudhury
DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus. - Ke Han
A review of methods for predicting DNA N6-methyladenine sites. - Yishu Wang
DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds. - Xue Li
MARPPI: boosting prediction of protein-protein interactions with multi-scale architecture residual network. - Bo Song
slORFfinder: a tool to detect open reading frames resulting from trans-splicing of spliced leader sequences. - Hai Yang
From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach. - Wubin Ding
Comparative epigenome analysis using Infinium DNA methylation BeadChips. - Kaili Wang
RLBind: a deep learning method to predict RNA-ligand binding sites. - Yifang Wei, Lingmei Li, Xin Zhao, Haitao Yang, Jian Sa, Hongyan Cao, Yuehua Cui
Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning. - Simon Orozco-Arias
Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes. - Lihua Wang, Tao Zhang, Lihong Yu, Chun-Hou Zheng, Wenguang Yin, Junfeng Xia
Deleterious synonymous mutation identification based on selective ensemble strategy. - Ze-Qun Zhang
CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data. - Yiming Xu
Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames. - Gabriel Cia
Critical review of conformational B-cell epitope prediction methods. - Siqin Zhang
DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms. - Shu-Guang Ge
Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping. - Huiyu Li, Chen Shen, Gongji Wang, Qinru Sun, Kai Yu, Zefeng Li, Xinggong Liang, Run Chen, Hao Wu, Fan Wang, Zhenyuan Wang, Chunfeng Lian
BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference. - Donghyo Kim, Doyeon Ha, Kwanghwan Lee, Heetak Lee, Inhae Kim, Sanguk Kim
An evolution-based machine learning to identify cancer type-specific driver mutations. - Zhangxin Zhao
Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data. - Zhiqiang Hu, Wenfeng Liu, Chenbin Zhang, Jiawen Huang, Shaoting Zhang
SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction. - Jianbo Fu
Label-free proteome quantification and evaluation. - Qi Liang, Wenxiang Zhang, Hao Wu, Bin Liu:
LncRNA-disease association identification using graph auto-encoder and learning to rank. - Alessia Buratin, Stefania Bortoluzzi
Systematic benchmarking of statistical methods to assess differential expression of circular RNAs. - Risa Karakida Kawaguchi
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. - Hailin Feng, Dongdong Jin, Jian Li, Yane Li, Quan Zou, Tongcun Liu:
Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations. - Bihan Shen, Fangyoumin Feng, Kunshi Li, Ping Lin, Liangxiao Ma
A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications. - Ruyun Hu
Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments. - Mengqi Luo
Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach. - Zechen Wang
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function. - Yu Zhao, Bing He
Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire. - Erik Christensen, Ping Luo, Andrei L. Turinsky, Mia Husic, Alaina Mahalanabis, Alaine Naidas, Juan Javier Díaz-Mejía, Michael Brudno, Trevor J. Pugh, Arun K. Ramani, Parisa Shooshtari
Evaluation of single-cell RNAseq labelling algorithms using cancer datasets. - Xiaosa Zhao, Jun Wu
Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction. - Lei Sun
SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level. - Xinyi Xu
Structure-preserved dimension reduction using joint triplets sampling for multi-batch integration of single-cell transcriptomic data. - Lu Yang
Benchmarking differential abundance analysis methods for correlated microbiome sequencing data. - Chun-Jie Liu
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. - Zerun Lin, Le Ou-Yang
Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning. - Chonghao Wang, Jing Zhang, Werner Pieter Veldsman
A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants. - Yitian Fang
AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning. - Correction to: VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.
- George Minadakis
Vir2Drug: a drug repurposing framework based on protein similarities between pathogens. - Andrew Cheng, Guanyu Hu, Wei Vivian Li:
Benchmarking cell-type clustering methods for spatially resolved transcriptomics data. - Wujuan Zhong
A fast and powerful linear mixed model approach for genotype-environment interaction tests in large-scale GWAS. - Yang Yue, Yongxuan Liu
Improving therapeutic synergy score predictions with adverse effects using multi-task heterogeneous network learning. - Marta Nazzari
CODA: a combo-Seq data analysis workflow. - Huan Hu, Zhen Feng
Modeling and analyzing single-cell multimodal data with deep parametric inference. - Sumeet Patiyal
Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile. - Yi-Zhou He
Incorporating higher order network structures to improve miRNA-disease association prediction based on functional modularity. - Zimeng Li, Shichao Zhu, Bin Shao, Xiangxiang Zeng, Tong Wang
DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning. - Lifang Yang
From single- to multi-omics: future research trends in medicinal plants. - Joel Defo
From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies? - Karel K. M. Koudijs
Validation of transcriptome signature reversion for drug repurposing in oncology. - Hai-Yun Wang, Jian-Ping Zhao
scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data. - Marwan Abdellah
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience. - Hong Wang, Chong Dai, Yuqi Wen, Xiaoqi Wang, Wenjuan Liu, Song He
GADRP: graph convolutional networks and autoencoders for cancer drug response prediction. - Dong Wang, Zhenxing Wu, Chao Shen
Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors. - Tianyuan Liu, Bohao Zou, Manman He, Yongfei Hu, Yiying Dou, Tianyu Cui, Puwen Tan, Shaobin Li, Shuan Rao, Yan Huang, Sixi Liu, Kaican Cai, Dong Wang
LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism. - Muhammad Nabeel Asim
DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method. - Chun-Qiu Xia
Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network. - Yongchao Luo
A novel strategy for designing the magic shotguns for distantly related target pairs. - Jingxuan Zhao, Jianqiang Sun, Stella C. Shuai, Qi Zhao
Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods. - Lingling Wang, Lei Xu, Zhe Wang
Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis. - Softya Sebastian
A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer's disease network. - Jingzhe Jiang
Virus classification for viral genomic fragments using PhaGCN2. - Zhi-Jian Zhou, Chen-Hui Yang, Sheng-Bao Ye, Xiao-Wei Yu, Ye Qiu
VirusRecom: an information-theory-based method for recombination detection of viral lineages and its application on SARS-CoV-2. - Yanqiang Han
A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein. - Cheng-Hong Yang
Dimensionality reduction approach for many-objective epistasis analysis. - Han Yu
IPPF-FE: an integrated peptide and protein function prediction framework based on fused features and ensemble models. - Yang Yang
A comprehensive review on knowledge graphs for complex diseases. - Nisha Bajiya
Advances in the field of phage-based therapy with special emphasis on computational resources. - Zhenshuang Tang, Lilin Yin
Development and application of an efficient genomic mating method to maximize the production performances of three-way crossbred pigs. - Siqi Chen, Xuhua Yan, Ruiqing Zheng
Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data. - Qilong Wu, Sheng-You Huang:
HCovDock: an efficient docking method for modeling covalent protein-ligand interactions. - Zixi Zheng, Yanyan Tan, Hong Wang, Shengpeng Yu, Tianyu Liu, Cheng Liang:
CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction. - Jing Liu, Xinghua Tang, Xiao Guan:
Grain protein function prediction based on self-attention mechanism and bidirectional LSTM. - Qitong Yuan, Keyi Chen, Yimin Yu, Nguyen-Quoc-Khanh Le
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding. - Kai Wang
GPDOCK: highly accurate docking strategy for metalloproteins based on geometric probability. - Haohong Zhang
Tracing human life trajectory using gut microbial communities by context-aware deep learning. - Wengang Wang, Hailin Chen
Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks. - Tian-Hao Li, Chun-Chun Wang
SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction. - Zhenling Peng
CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information. - Mei Li
Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction. - Guanqun Meng, Wen Tang, Emina Huang, Ziyi Li, Hao Feng
A comprehensive assessment of cell type-specific differential expression methods in bulk data. - Shuaiqun Wang, Kai Zheng, Wei Kong, Ruiwen Huang, Lulu Liu, Gen Wen, Yaling Yu:
Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease. - Guobo Xie, Rui-Bin Chen, Zhiyi Lin
Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation. - Yingjun Ma
Logistic tensor decomposition with sparse subspace learning for prediction of multiple disease types of human-virus protein-protein interactions. - Anna Dal Molin, Caterina Tretti Parenzan, Enrico Gaffo
Discovery of fusion circular RNAs in leukemia with KMT2A::AFF1 rearrangements by the new software CircFusion. - Shehab Sarar Ahmed
Succinylated lysine residue prediction revisited. - Jiayu Shang
PhaTYP: predicting the lifestyle for bacteriophages using BERT. - Hongwei Chen
AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein-protein interaction network. - Giulia Pais
ISAnalytics enables longitudinal and high-throughput clonal tracking studies in hematopoietic stem cell gene therapy applications. - Yetian Fan
A Bayesian model for identifying cancer subtypes from paired methylation profiles. - Min Li
GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation. - Weibin Huang
Personalized immune subtypes based on machine learning predict response to checkpoint blockade in gastric cancer. - Qiao Liu
Deep generative modeling and clustering of single cell Hi-C data. - Yan Kang, Yulong Xu, Xinchao Wang, Bin Pu, Xuekun Yang, Yulong Rao, Jianguo Chen:
HN-PPISP: a hybrid network based on MLP-Mixer for protein-protein interaction site prediction. - Yao Lu, Zhiqiang Pang, Jianguo Xia
Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data. - Kanggeun Lee, Dongbin Cho, Jinho Jang
RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction. - Bo Yang, Hailin Chen
Predicting circRNA-drug sensitivity associations by learning multimodal networks using graph auto-encoders and attention mechanism. - Wenxuan Deng, Bolun Li
A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. - Upendra Kumar Pradhan
PlDBPred: a novel computational model for discovery of DNA binding proteins in plants. - Peicong Lin, Yumeng Yan, Sheng-You Huang:
DeepHomo2.0: improved protein-protein contact prediction of homodimers by transformer-enhanced deep learning. - Mengjie Shao
Pdif-mediated antibiotic resistance genes transfer in bacteria identified by pdifFinder. - Sanghun Lee
A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets. - Tim D. Rose
Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data. - Wei Lan
Benchmarking of computational methods for predicting circRNA-disease associations. - Xiao Fan
SHINE: protein language model-based pathogenicity prediction for short inframe insertion and deletion variants. - Haoran Gong
Integrated mRNA sequence optimization using deep learning. - Xi-Liang Zhu
Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning. - Shujun He
RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning. - Guo-Hua Yuan
RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization. - Xin Zhang, Lesong Wei, Xiucai Ye
SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning. - Kuo Yang, Yuxia Yang, Shuyue Fan, Jianan Xia, Qiguang Zheng, Xin Dong
DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning.
Volume 24, Number 2, March 2023
- Xuebin Wang
GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences. - Yuepeng Jiang
Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires. - Yuepeng Jiang
TEINet: a deep learning framework for prediction of TCR-epitope binding specificity. - Lingyu Cui
Unsupervised construction of gene regulatory network based on single-cell multi-omics data of colorectal cancer. - Yuyao Zhai, Liang Chen, Minghua Deng
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery. - Lei Wang
NeuroPred-PLM: an interpretable and robust model for neuropeptide prediction by protein language model. - Ming-Xiao Zhao
Protein phosphorylation database and prediction tools. - Xingzhong Zhao
Deciphering the genetic architecture of human brain structure and function: a brief survey on recent advances of neuroimaging genomics. - Rebecca Elizabeth Kattan, Deena Ayesh, Wenqi Wang
Analysis of affinity purification-related proteomic data for studying protein-protein interaction networks in cells. - Yan Huang, Stefan Wuchty, Yuan Zhou, Ziding Zhang
SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network. - Pei-Dong Zhang
High-accuracy protein model quality assessment using attention graph neural networks. - Jing Zhao
Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data. - Zhen Tian
Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy. - Jianchang Hu
A review on longitudinal data analysis with random forest. - Peiran Jiang
A hybrid positive unlabeled learning framework for uncovering scaffolds across human proteome by measuring the propensity to drive phase separation. - Yunda Si, Chengfei Yan
Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models. - Marc Oeller
Sequence-based prediction of pH-dependent protein solubility using CamSol. - Hsin-Hua Chen, Chun-Wei Hsueh, Chia-Hwa Lee, Ting-Yi Hao, Tzu-Ying Tu, Lan-Yun Chang, Jih-Chin Lee, Chun-Yu Lin
SWEET: a single-sample network inference method for deciphering individual features in disease. - Marianna Milano, Mario Cannataro:
Network models in bioinformatics: modeling and analysis for complex diseases. - Sagar Gupta, Ravi Shankar
miWords: transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes. - Yansen Su
Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data. - Ziyu Tao
The repertoire of copy number alteration signatures in human cancer. - Sosie Yorki
Comparison of long- and short-read metagenomic assembly for low-abundance species and resistance genes. - Diane Duroux, Kristel Van Steen:
netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA. - Yiwen Wang
PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data. - Xiangwen Ji
Deciphering gene contributions and etiologies of somatic mutational signatures of cancer. - Jiayuan Zhong
SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems. - Joongho Lee, Minsoo Kim, Keunsoo Kang, Chul-Su Yang
Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing. - Han Sun
multiMiAT: an optimal microbiome-based association test for multicategory phenotypes. - Correction to: mCNN-ETC: identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences.
- Wei Zhang
IMperm: a fast and comprehensive IMmune Paired-End Reads Merger for sequencing data. - Tao Deng
A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis. - Li Liu
A comprehensive review of bioinformatics tools for chromatin loop calling. - Kahn Rhrissorrakrai
Lesion Shedding Model: unraveling site-specific contributions to ctDNA. - Saisai Tian
Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM. - Kai-Yue Ji, Chong Liu, Zhao-Qian Liu, Yafeng Deng, Tingjun Hou
Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? - Dario Romagnoli, Agostina Nardone, Francesca Galardi, Marta Paoli, Francesca De Luca, Chiara Biagioni, Gian Marco Franceschini
MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples. - Haiwei Zhou, Wenxi Tan, Shao-Ping Shi
DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism. - Yu'e Huang, Shunheng Zhou, Haizhou Liu, Xu Zhou, Mengqin Yuan, Fei Hou, Sina Chen, Jiahao Chen, Lihong Wang, Wei Jiang
DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network. - Yuansong Zeng
Identifying spatial domain by adapting transcriptomics with histology through contrastive learning. - Juhua Pu, Bingchen Wang, Xingwu Liu, Lingxi Chen
SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency. - Zhenmiao Zhang
Benchmarking genome assembly methods on metagenomic sequencing data. - Jingxuan Bao
Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis. - Correction to: kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes.
- Yutong Yu, Pengju Ding, Hongli Gao, Guozhu Liu, Fa Zhang, Bin Yu
Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction. - Weizhong Zhao, Xueling Yuan, Xianjun Shen, Xingpeng Jiang, Chuan Shi, Tingting He, Xiaohua Hu:
Improving drug-drug interactions prediction with interpretability via meta-path-based information fusion. - Mang Liang, Sheng Cao, Tianyu Deng, Lili Du, Keanning Li, Bingxing An, Yueying Du, Lingyang Xu
MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits. - Yuan-Qin Huang, Ping Sun, Yi Chen, Huanxiang Liu
Bioinformatics toolbox for exploring target mutation-induced drug resistance. - Qiushi Cao
Designing antimicrobial peptides using deep learning and molecular dynamic simulations. - Zhou Yao, Wenjing Zhang, Peng Song, Yuxue Hu, Jianxiao Liu
DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences. - Jie Hao
scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data. - Lianming Du
Dockey: a modern integrated tool for large-scale molecular docking and virtual screening. - Xiang Liu, Huitao Feng, Zhi Lü, Kelin Xia:
Persistent Tor-algebra for protein-protein interaction analysis. - Peike Wu, Renliang Sun, Aamir Fahira, Yongzhou Chen, Huiting Jiangzhou, Ke Wang
DROEG: a method for cancer drug response prediction based on omics and essential genes integration. - Harsh Sharma
Prediction of transcript structure and concentration using RNA-Seq data. - Yongtian Wang
Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information. - Tingting Zhao, Guangyu Zhu, Harsh Vardhan Dubey, Patrick Flaherty
Identification of significant gene expression changes in multiple perturbation experiments using knockoffs. - Connor H. Knight, Faraz Khan, Ankit Patel, Upkar S. Gill, Jessica Okosun, Jun Wang
IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline. - Xinru Ruan
MSGCL: inferring miRNA-disease associations based on multi-view self-supervised graph structure contrastive learning. - Hui Liu
MDBuilder: a PyMOL plugin for the preparation of molecular dynamics simulations. - Bin Liu
Fine-grained selective similarity integration for drug-target interaction prediction. - Lianhe Zhao
Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network. - Correction to: Prediction of transcript structure and concentration using RNA-Seq data.
- Yueh-Hua Tu, Hsueh-Fen Juan
Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts. - Sijia Wu
Genetic control of RNA editing in neurodegenerative disease. - Rulan Wang, Chia-Ru Chung
Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences. - Ina Bang, Sang-Mok Lee
Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling. - Xingxin Pan
AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning. - Tao Tang
Machine learning on protein-protein interaction prediction: models, challenges and trends. - Yamao Chen, Shengyu Zhou, Ming Li, Fangqing Zhao
STEEL enables high-resolution delineation of spatiotemporal transcriptomic data. - Chenxu Pan, René Rahn, David Heller, Knut Reinert
Linear: a framework to enable existing software to resolve structural variants in long reads with flexible and efficient alignment-free statistical models. - A Samet Özdilek
ProFAB - open protein functional annotation benchmark. - Zimo Huang, Jun Wang, Xudong Lu, Azlan Mohd Zain
scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network. - Mingzhu Liu, Jian Zhou, Qilemuge Xi, Yuchao Liang, Haicheng Li, Pengfei Liang, Yuting Guo, Ming Liu, Temuqile Temuqile, Lei Yang
A computational framework of routine test data for the cost-effective chronic disease prediction. - María Isabel Alcoriza-Balaguer, Juan Carlos García-Cañaveras
FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism. - Xiaoti Jia, Pei Zhao, Fuyi Li
ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning. - Qiao Ning
AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA-disease associations identification. - Zi-Jun Yan, Chu-Ting Yu, Lei Chen, Hong-Yang Wang
Development of a TMErisk model based on immune infiltration in tumour microenvironment to predict prognosis of immune checkpoint inhibitor treatment in hepatocellular carcinoma. - Bertrand Jern Han Wong
PROSE: phenotype-specific network signatures from individual proteomic samples. - Yanpeng Zhao
Identification of metal ion-binding sites in RNA structures using deep learning method. - Fei Zhu
PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk. - Chirag Parsania
FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery. - Victoria A Kobets, Sergey V. Ulianov, Aleksandra A. Galitsyna
HiConfidence: a novel approach uncovering the biological signal in Hi-C data affected by technical biases. - Le Yuan
HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer. - Tengwei Zhong
eccDNA Atlas: a comprehensive resource of eccDNA catalog. - Andrew Nakamura
Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps. - Zhiwei Cheng
HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol. - Xiao Wang
Deepro-Glu: combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sites. - Binjie Guo, Hanyu Zheng, Haohan Jiang, Xiaodan Li, Naiyu Guan, Yanming Zuo, Yicheng Zhang, Hengfu Yang, Xuhua Wang
Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy. - Yanping Zhao, Kui Wang, Gang Hu
DIST: spatial transcriptomics enhancement using deep learning. - Shukai Gu
Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning? - Ran Zhang, Zhanjie Wang, Xuezhi Wang, Zhen Meng, Wenjuan Cui:
MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug-target interaction prediction. - Minwoo Pak
Improved drug response prediction by drug target data integration via network-based profiling. - Yuqi Sheng, Jiashuo Wu
iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network. - Fei-Liao Lai
Auto-Kla: a novel web server to discriminate lysine lactylation sites using automated machine learning. - Jilong Bian, Xi Zhang, Xiying Zhang, Dali Xu, Guohua Wang:
MCANet: shared-weight-based MultiheadCrossAttention network for drug-target interaction prediction. - Renfei Ma, Shangfu Li, Luca Parisi, Wenshuo Li, Hsien-Da Huang, Tzong-Yi Lee
Holistic similarity-based prediction of phosphorylation sites for understudied kinases.
Volume 24, Number 3, May 2023
- Federico Gossi
Matching single cells across modalities with contrastive learning and optimal transport. - Correction to: Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design.
- Akshay Khanduja
ProsmORF-pred: a machine learning-based method for the identification of small ORFs in prokaryotic genomes. - Fengqi Ge, Chunxiang Peng, Xinyue Cui, Yuhao Xia, Guijun Zhang
Inter-domain distance prediction based on deep learning for domain assembly. - Ryuji Hamamoto
Analysis of super-enhancer using machine learning and its application to medical biology. - Ruiqiang Lu, Jun Wang, Pengyong Li, Yuquan Li, Shuoyan Tan, Yiting Pan, Huanxiang Liu, Peng Gao, Guotong Xie, Xiaojun Yao:
Improving drug-target affinity prediction via feature fusion and knowledge distillation. - Hongjia Liu, Huamei Li, Amit Sharma
scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets. - Ke Shen
Translational informatics for human microbiota: data resources, models and applications. - Zheng Ye, Shaohao Li, Xue Mi, Baoyi Shao, Zhu Dai, Bo Ding, Songwei Feng, Bo Sun, Yang Shen, Zhongdang Xiao:
STMHCpan, an accurate Star-Transformer-based extensible framework for predicting MHC I allele binding peptides. - Hui Zhang, Hongjia Li, Fa Zhang, Ping Zhu:
A strategy combining denoising and cryo-EM single particle analysis. - Feng Zhang
InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop. - Ge Zhang, Zhijie Gao, Chaokun Yan, Jianlin Wang, Wenjuan Liang, Junwei Luo, Huimin Luo
KGANSynergy: knowledge graph attention network for drug synergy prediction. - Ying Liao
DeepMiceTL: a deep transfer learning based prediction of mice cardiac conduction diseases using early electrocardiograms. - Mingliang Dou, Jiaqi Ding, Genlang Chen, Junwen Duan, Fei Guo, Jijun Tang:
IK-DDI: a novel framework based on instance position embedding and key external text for DDI extraction. - Niloofar Yousefi, Mehdi Yazdani-Jahromi
BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing. - Joshua Teitz
Potential of dissimilarity measure-based computation of protein thermal stability data for determining protein interactions. - Qi Lu, Ruihan Zhang, Hongyuan Zhou, Dongxuan Ni, Weilie Xiao, Jin Li:
MetaHMEI: meta-learning for prediction of few-shot histone modifying enzyme inhibitors. - Xiao Wang
DaDL-SChlo: protein subchloroplast localization prediction based on generative adversarial networks and pre-trained protein language model. - Ying-Ying Zhang, De-Min Liang, Pu-Feng Du
iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network. - Xin Wang
miProBERT: identification of microRNA promoters based on the pre-trained model BERT. - Hang Zhao
Evaluation of graphical models for multi-group metabolomics data. - Pablo Acera Mateos
Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning. - Minghao Jiang
A comprehensive benchmarking of differential splicing tools for RNA-seq analysis at the event level. - Tao Wu, Yao Dai, Yue Xu, Jie Zheng, Shuting Chen, Yinuo Zhang, Peng Tian, Xiaoqi Zheng, Haiyun Wang:
ExosomePurity: tumour purity deconvolution in serum exosomes based on miRNA signatures. - Correction to: Quantum computing algorithms: getting closer to critical problems in computational biology.
- Zhenjiao Du
UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity. - Zengrui Guan, Zhenran Jiang:
Transformer-based anti-noise models for CRISPR-Cas9 off-target activities prediction. - Jing Jiang
Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder. - Yuting Zhou
AGAT-PPIS: a novel protein-protein interaction site predictor based on augmented graph attention network with initial residual and identity mapping. - Kaixuan Xiao, Yu Wang, Kangning Dong, Shihua Zhang:
SmartGate is a spatial metabolomics tool for resolving tissue structures. - Fatemeh Farhadi, Mohammad Allahbakhsh
DiMo: discovery of microRNA motifs using deep learning and motif embedding. - Ajay Subbaroyan
Leveraging developmental landscapes for model selection in Boolean gene regulatory networks. - Feng Chen, Yubo Bai, Chunhe Li
Estimation of non-equilibrium transition rate from gene expression data. - Jun Wang, Xi Chen, Zhengtian Wu, Maozu Guo, Guoxian Yu
Cooperative driver pathways discovery by multiplex network embedding. - Xiaoqiang Huang, Jun Zhou, Dongshan Yang
Decoding CRISPR-Cas PAM recognition with UniDesign. - Yijia Li, Jonathan Nguyen, David C. Anastasiu, Edgar A. Arriaga:
CosTaL: an accurate and scalable graph-based clustering algorithm for high-dimensional single-cell data analysis. - Jian Liu, Pingjing Li, Jialiang Sun, Jun Guo:
LPAD: using network construction and label propagation to detect topologically associating domains from Hi-C data. - Junwei Luo, Ting Guan, Guolin Chen, Zhonghua Yu, Haixia Zhai, Chaokun Yan, Huimin Luo:
SLHSD: hybrid scaffolding method based on short and long reads. - Anna Klimovskaia Susmelj
Poincaré maps for visualization of large protein families. - Yiyou Song, Yue Wang
Multi-task adaptive pooling enabled synergetic learning of RNA modification across tissue, type and species from low-resolution epitranscriptomes. - Tong Zhang, Shao-Wu Zhang, Ming-Yu Xie, Yan Li:
A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes. - Daoyu Duan
Evaluation of epitranscriptome-wide N6-methyladenosine differential analysis methods. - Hongli Gao, Bin Zhang
A universal framework for single-cell multi-omics data integration with graph convolutional networks. - Yang Li
End-to-end interpretable disease-gene association prediction. - Hailin Wei, Tong Han, Taiwen Li
SCREE: a comprehensive pipeline for single-cell multi-modal CRISPR screen data processing and analysis. - Lun Li
McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks. - Xin-Fei Wang
A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks. - Jiaqi Luo
Quantitative annotations of T-Cell repertoire specificity. - Kechi Fang, Chuan Li, Jing Wang:
An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning. - Yuanchun Zhao, Xingqi Chen, Jiajia Chen, Xin Qi:
Decoding Connectivity Map-based drug repurposing for oncotherapy. - Weihe Dong, Qiang Yang
Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network. - Qikai Niu
TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms. - Yongfan Ming, Wenkang Wang
A review of enzyme design in catalytic stability by artificial intelligence. - An-phi Nguyen
FLAN: feature-wise latent additive neural models for biological applications. - Martin Bishop:
Editorial. - Sikang Chen, Jian Gao, Jiexuan Chen, Yufeng Xie, Zheyuan Shen, Lei Xu, Jinxin Che, Jian Wu, Xiaowu Dong:
ClusterX: a novel representation learning-based deep clustering framework for accurate visual inspection in virtual screening. - Mengsha Tong, Yuxiang Lin, Wenxian Yang
Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer. - Guangshuai Wang, Tao Wu
TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning. - Camilo Rebolledo
Computational approaches for circRNAs prediction and in silico characterization. - Mohammad Rizwan Alam, Kyung Jin Seo
Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers. - Koushik Mallick, Sikim Chakraborty, Saurav Mallik, Sanghamitra Bandyopadhyay:
A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection. - Long-Hao Jia, Yingjian Wu
A survey on computational strategies for genome-resolved gut metagenomics. - Meng Wang, Lukasz A. Kurgan
A comprehensive assessment and comparison of tools for HLA class I peptide-binding prediction. - Ruyi Chen
ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species. - Kai-Li Chang
Short human eccDNAs are predictable from sequences. - Wei Xu, Teng Wang
Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types. - Yan Wang, Chenxu Xuan, Hanwen Wu, Bai Zhang, Tao Ding, Jie Gao:
P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network. - Teng Liu, Zhao-Yu Fang, Xin Li, Li-Ning Zhang, Dong-Sheng Cao, Mingzhu Yin
Graph deep learning enabled spatial domains identification for spatial transcriptomics. - Yigang Chen
Quantitative model for genome-wide cyclic AMP receptor protein binding site identification and characteristic analysis. - Hongning Zhang
SoCube: an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data. - Bing Li, Tian Wang, Min Qian, Shuang Wang:
MKMR: a multi-kernel machine regression model to predict health outcomes using human microbiome data. - Yushan Qiu
SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data. - Sumit Mukherjee
A computational approach for the identification of distant homologs of bacterial riboswitches based on inverse RNA folding. - Ke Xu, Chinwang Cheong, Werner Pieter Veldsman
Accurate and interpretable gene expression imputation on scRNA-seq data using IGSimpute. - Yan Liu, Guo Wei, Chen Li, Long-Chen Shen
TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level. - Qianmu Yuan
Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion. - Yue Cao
Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data. - Peilin Jia
Benchmark of embedding-based methods for accurate and transferable prediction of drug response. - Zhenhua Yu
rcCAE: a convolutional autoencoder method for detecting intra-tumor heterogeneity and single-cell copy number alterations. - Prem Singh Bist
Sars-escape network for escape prediction of SARS-COV-2. - Jiashu Liu, Cui-Xiang Lin, Xiaoqi Zhang, Zongxuan Li, Wenkui Huang, Jin Liu, Yuanfang Guan
Computational approaches for detecting disease-associated alternative splicing events. - Bruna Moreira da Silva
epitope1D: accurate taxonomy-aware B-cell linear epitope prediction. - Marek Justyna
Machine learning for RNA 2D structure prediction benchmarked on experimental data. - Manuel Tognon
A survey on algorithms to characterize transcription factor binding sites. - Fatima Noor
Machine learning for synergistic network pharmacology: a comprehensive overview. - Wei Liu, Yu Yang, Xu Lu, Xiangzheng Fu
NSRGRN: a network structure refinement method for gene regulatory network inference. - Zeinab Sherkatghanad, Moloud Abdar, Jérémy Charlier, Vladimir Makarenkov
Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.
Volume 24, Number 4, July 2023
- Mandy Ibéné, Audrey Legendre, Guillaume Postic
C-RCPred: a multi-objective algorithm for interactive secondary structure prediction of RNA complexes integrating user knowledge and SHAPE data. - Juntao Chen, Jiannan Chao, Huan Liu, Fenglong Yang, Quan Zou, Furong Tang:
WMSA 2: a multiple DNA/RNA sequence alignment tool implemented with accurate progressive mode and a fast win-win mode combining the center star and progressive strategies. - Kevin E. Wu
Machine learning modeling of RNA structures: methods, challenges and future perspectives. - Rongtao Zheng
Large-scale predicting protein functions through heterogeneous feature fusion. - Taha ValizadehAslani
PharmBERT: a domain-specific BERT model for drug labels. - Yu Zhao, Xiaona Su, Weitong Zhang
SC-AIR-BERT: a pre-trained single-cell model for predicting the antigen-binding specificity of the adaptive immune receptor. - Guoxing Cai
MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis. - Anil Prakash, Moinak Banerjee
An interpretable block-attention network for identifying regulatory feature interactions. - Ke Yan
iDRPro-SC: identifying DNA-binding proteins and RNA-binding proteins based on subfunction classifiers. - Anja Conev
EnGens: a computational framework for generation and analysis of representative protein conformational ensembles. - Muhammad Toseef
Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results. - Weijia Kong
ProJect: a powerful mixed-model missing value imputation method. - Gaofei Jiang, Jiaxuan Zhang, Yaozhong Zhang, Xinrun Yang
DCiPatho: deep cross-fusion networks for genome scale identification of pathogens. - Pengju Ding, Yifei Wang, Xinyu Zhang, Xin Gao, Guozhu Liu, Bin Yu
DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape. - Qiang He
Improving the identification of miRNA-disease associations with multi-task learning on gene-disease networks. - Marian Gimeno
Precision oncology: a review to assess interpretability in several explainable methods. - Chen-Di Han, Chun-Chun Wang
MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction. - Juntao Deng
IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity. - Yongkang Zhan
iBT-Net: an incremental broad transformer network for cancer drug response prediction. - Dayu Hu
scDFC: A deep fusion clustering method for single-cell RNA-seq data. - Shuangge Ma
Editorial. - Tao Bai, Ke Yan
DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks. - Meiyu Duan
Orchestrating information across tissues via a novel multitask GAT framework to improve quantitative gene regulation relation modeling for survival analysis. - Maohua Yang
Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening. - Yanglan Gan, Yuhan Chen, Guangwei Xu, Wenjing Guo, Guobing Zou:
Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data. - Boshen Wang
Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations. - Xuan Lin
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction. - Xiaoqing Peng
CATAD: exploring topologically associating domains from an insight of core-attachment structure. - Hongjun Chen, Yekai Zhou
Network integration and protein structural binding analysis of neurodegeneration-related interactome. - Zhongyu Wang, Zhaohong Deng, Wei Zhang, Qiongdan Lou, Kup-Sze Choi, Zhisheng Wei, Lei Wang, Jing Wu:
MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction. - Qingchun Liu, Kai Song:
ProgCAE: a deep learning-based method that integrates multi-omics data to predict cancer subtypes. - Kai Li, Ping Zhang
iEnhance: a multi-scale spatial projection encoding network for enhancing chromatin interaction data resolution. - Natalia A Szulc, Zuzanna Mackiewicz
Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA. - Théo Falgarone, Etienne Villain, François D. Richard, Zarifa Osmanli, Andrey V. Kajava
Census of exposed aggregation-prone regions in proteomes. - Qi Yang, Zhaochun Xu
An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation. - Bo Chen, Ziwei Xie
Improved the heterodimer protein complex prediction with protein language models. - José Luis Ruiz
From contigs towards chromosomes: automatic improvement of long read assemblies (ILRA). - Shuang Yang, Weikang Gong, Tong Zhou, Xiaohan Sun, Lei Chen, Wenxue Zhou, Chunhua Li:
emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model. - Han Li
Improving comparative analyses of Hi-C data via contrastive self-supervised learning. - Göknur Giner
A systematic review of computational methods for designing efficient guides for CRISPR DNA base editor systems. - Siqi Gao, Hanwen Zhu, Kangwen Cai, Leiqin Liu, Zhiqiang Zhang, Yi Ding, Yaochen Xu, Xiaoqi Zheng, Jiantao Shi
TRAmHap: accurate prediction of transcriptional activity from DNA methylation haplotypes in bisulfite-sequencing data. - Shudong Wang
Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm. - Song Zhai, Bin Guo, Baolin Wu, Devan V. Mehrotra, Judong Shen
Integrating multiple traits for improving polygenic risk prediction in disease and pharmacogenomics GWAS. - Ze-Ying Feng
DKADE: a novel framework based on deep learning and knowledge graph for identifying adverse drug events and related medications. - Yifan Yang
E-value: a superior alternative to P-value and its adjustments in DNA methylation studies. - Kerui Peng, Theodore S. Nowicki, Katie Campbell, Mohammad Vahed, Dandan Peng, Yiting Meng, Anish Nagareddy, Yu-Ning Huang
Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing. - Yidong Song, Qianmu Yuan, Sheng Chen, Ken Chen
Fast and accurate protein intrinsic disorder prediction by using a pretrained language model. - Xenia Lainscsek, Leila Taher
Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA. - Jeremie Theddy Darmawan
MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction. - Liugen Wang
Predicting potential microbe-disease associations based on multi-source features and deep learning. - Guang-Xing He, Jun Liu, Dong Liu, Guijun Zhang
GraphGPSM: a global scoring model for protein structure using graph neural networks. - Wei Liu
MPCLCDA: predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning. - Chun He, Xinhai Ye
DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins. - Yu-Hsin Chen, Kuan-Hao Chao
A feature extraction free approach for protein interactome inference from co-elution data. - Hui Yu
Attention-based cross domain graph neural network for prediction of drug-drug interactions. - Mingxiang Zhang, Hongli Gao, Xin Liao, Baoxing Ning, Haiming Gu, Bin Yu
DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism. - Leqi Tian
An integrated deep learning framework for the interpretation of untargeted metabolomics data. - Ruoxing Li, Mehmet Altan, Alexandre Reuben, Ruitao Lin, John V. Heymach, Hai Tran, Runzhe Chen, Latasha Little, Shawna Hubert, Jianjun Zhang, Ziyi Li:
A novel statistical method for decontaminating T-cell receptor sequencing data. - Cheng Wang, Chuang Yuan, Yahui Wang, Ranran Chen, Yuying Shi, Tao Zhang
MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders. - Karin Elimelech-Zohar, Yaron Orenstein
An overview on nucleic-acid G-quadruplex prediction: from rule-based methods to deep neural networks. - Jing Xu
CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data. - Junyu Yan
ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing. - Minjian Yang, Hanyu Sun, Xue Liu, Xi Xue, Yafeng Deng, Xiaojian Wang:
CMGN: a conditional molecular generation net to design target-specific molecules with desired properties. - Shaofeng Liao, Yujun Zhang, Yifei Qi
Evaluation of sequence-based predictors for phase-separating protein. - Kengo Sato
Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery. - Ran Liu
A Bayesian approach to estimate MHC-peptide binding threshold. - Yan Zhu
TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters. - Correction to: DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences.
- Qiaozhen Meng, Fei Guo, Jijun Tang:
Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model. - Haojie Lu, Shuo Zhang, Zhou Jiang, Ping Zeng
Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations. - Jing Xu, Fuyi Li
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities. - Correction: Comparison and benchmark of structural variants detected from long read and long-read assembly.
- Hao Zhu, Tong Liu, Zheng Wang:
scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data. - Chuanchao Zhang
Spatially aware self-representation learning for tissue structure characterization and spatial functional genes identification. - Sheng Hu Qian
Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster. - Jinhang Wei, Linlin Zhuo
GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning. - Jiadong Lin, Peng Jia
Comparison and benchmark of structural variants detected from long read and long-read assembly. - Weiqi Zhai, Xiaodi Huang
Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.
Volume 24, Number 5, September 2023
- Rui Fan
Defining the single base importance of human mRNAs and lncRNAs. - Baiqing Li
3D based generative PROTAC linker design with reinforcement learning. - Emily L. Leventhal
Letter to the editor: testing the effectiveness of MyPROSLE in classifying patients with lupus nephritis. - Xujun Zhang
ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions. - Hendrick Gao-Min Lim
COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2. - Tao Cui
A comprehensive assessment of hurdle and zero-inflated models for single cell RNA-sequencing analysis. - He Wang
ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction. - Ailin Xie
Self-supervised learning with chemistry-aware fragmentation for effective molecular property prediction. - Jiayi Liu
Differential variability analysis of single-cell gene expression data. - Chengming Zhang
Contrastively generative self-expression model for single-cell and spatial multimodal data. - Zixiao Wang
Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations. - Shangru Jia
scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning. - David Martínez-Enguita
NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures. - Lejin Tian
AtacAnnoR: a reference-based annotation tool for single cell ATAC-seq data. - Shengming Zhou
PredLLPS_PSSM: a novel predictor for liquid-liquid protein separation identification based on evolutionary information and a deep neural network. - Ko Abe
UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data. - Yuhang Liu, Zixuan Wang, Hao Yuan, Guiquan Zhu, Yongqing Zhang
HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction. - Jinyang Wu
KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations. - Jingjing Wang
Benchmarking multi-platform sequencing technologies for human genome assembly. - Dohun Yi, Jin-Wu Nam
Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. - Xiaobo Yang, Bei Zhang, Siqi Wang, Ye Lu, Kaixian Chen, Cheng Luo
OTTM: an automated classification tool for translational drug discovery from omics data. - Pora Kim
Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer - bioinformatics study for a potential link to MMEJ. - Jiali Pang
A denoised multi-omics integration framework for cancer subtype classification and survival prediction. - Shudong Wang
Identifying potential small molecule-miRNA associations via Robust PCA based on γ-norm regularization. - Guiying Wu
DELFMUT: duplex sequencing-oriented depth estimation model for stable detection of low-frequency mutations. - Juan D. Henao
Multi-omics regulatory network inference in the presence of missing data. - Yuchi Qiu
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models. - Yuansheng Liu, Xiangzhen Shen, Yongshun Gong, Yiping Liu, Bosheng Song
Sequence Alignment/Map format: a comprehensive review of approaches and applications. - Hunyong Cho, Yixiang Qu
Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data. - Wenlan Chen, Hong Wang, Cheng Liang:
Deep multi-view contrastive learning for cancer subtype identification. - Weixu Wang
Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion. - Nan Sheng
Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases. - S. M. Bargeen Alam Turzo, Justin T. Seffernick, Sergey Lyskov, Steffen Lindert
Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver. - Tasbiraha Athaya, Rony Chowdhury Ripan, Xiaoman Li
Multimodal deep learning approaches for single-cell multi-omics data integration. - Marc Horlacher
A systematic benchmark of machine learning methods for protein-RNA interaction prediction. - Hongyan Gao, Jianqiang Sun, Yukun Wang, Yuer Lu, Liyu Liu, Qi Zhao
Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization. - Yang Yue, Shu Li
MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions. - Huiguang Yi
A fast and globally optimal solution for RNA-seq quantification. - Tong Liu, Zheng Wang:
HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM. - Md. Rezaul Karim
Explainable AI for Bioinformatics: Methods, Tools and Applications. - Mona Nourbakhsh
A workflow to study mechanistic indicators for driver gene prediction with Moonlight. - Jun Wang
RNA trafficking and subcellular localization - a review of mechanisms, experimental and predictive methodologies. - Nguyen-Quoc-Khanh Le
Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection. - Kai Chen, Xiaodong Zhu, Jiahao Wang, Ziqi Zhao, Lei Hao, Xinsheng Guo, Yuanning Liu:
MFPred: prediction of ncRNA families based on multi-feature fusion. - Tianjiao Zhang
DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome. - Shijie Xie, Xiaojun Xie, Xin Zhao, Fei Liu, Yiming Wang, Jihui Ping, Zhiwei Ji
HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction. - Jianfeng Sun
TMKit: a Python interface for computational analysis of transmembrane proteins. - Tingting Zhao, Lihua Cao, Jiafu Ji, David K. Chang
ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting. - Runzhou Yu
HMMPolish: a coding region polishing tool for TGS-sequenced RNA viruses. - Zhongying Ru
A dual-modal graph learning framework for identifying interaction events among chemical and biotech drugs. - Bo Wang, Jiawei Luo, Ying Liu, Wanwan Shi, Zehao Xiong
Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism. - Jeonghyeon Gu
A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning. - Andrea Raffo
The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data. - Qingyong Wang
AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration. - Lindsay Lee
SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single-cell Hi-C data. - Jun Wu
HiFun: homology independent protein function prediction by a novel protein-language self-attention model. - Yanping Zeng, Yongxin He, Ruiqing Zheng
Inferring single-cell gene regulatory network by non-redundant mutual information. - Jian Gao, Zheyuan Shen, Yufeng Xie, Jialiang Lu, Yang Lu
TransFoxMol: predicting molecular property with focused attention. - Shen Han
HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction. - Hai Yang
InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data. - Na Lu
Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data. - Robert Wang, Ingo Helbig
Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis. - Yan Li
PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer. - Pingjing Li, Hong Liu, Jialiang Sun, Jianguo Lu
HiBrowser: an interactive and dynamic browser for synchronous Hi-C data visualization. - Yao-zhong Zhang
Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning. - Xuejing Shi, Juntong Zhu, Yahui Long, Cheng Liang:
Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks. - Quentin Blampey
A biology-driven deep generative model for cell-type annotation in cytometry. - Shudong Wang
Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA-Disease Associations Prediction. - Lin Li
Single-cell causal network inferred by cross-mapping entropy.
Volume 24, Number 6, September 2023
- Wentai Ma, Leisheng Shi
A fast and accurate method for SARS-CoV-2 genomic tracing. - Yongjian Yang
Interpretable modeling of time-resolved single-cell gene-protein expression with CrossmodalNet. - Takako Mochizuki, Mika Sakamoto
A practical assembly guideline for genomes with various levels of heterozygosity. - Pengfei Gao, Haonan Zhao, Zheng Luo, Yifan Lin, Wanjie Feng, Yaling Li, Fanjiang Kong, Xia Li, Chao Fang, Xutong Wang:
SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding. - Zhuo Wang
A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology. - Wentao Zhang
Fuse feeds as one: cross-modal framework for general identification of AMPs. - Zhe Lin, Yangmei Qin, Hao Chen
TransIntegrator: capture nearly full protein-coding transcript variants via integrating Illumina and PacBio transcriptomes. - Andy J. Wu
Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches. - Saisai Tian
COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming. - Musun Park
Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data for improving in silico herbal medicine experimentations. - Yu-Ning Huang
Response to 'comment on rigorous benchmarking of T cell receptor repertoire profiling methods for cancer RNA sequencing' by Davydov A.N.; Bolotin D.A.; Poslavsky S. V. and Chudakov D.M. - Alison K. Adams
Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes. - Yidong Song, Qianmu Yuan
Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures. - Marie-Pier Scott-Boyer, Pascal Dufour, François Belleau, Régis Ongaro-Carcy, Clément Plessis, Olivier Périn, Arnaud Droit
Use of Elasticsearch-based business intelligence tools for integration and visualization of biological data. - Anupam Gautam
Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification. - Haochen Li
Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. - Alexey N. Davydov, Dmitry Bolotin, Stanislav V. Poslavsky
Comment on 'rigorous benchmarking of T cell receptor repertoire profiling methods for cancer RNA sequencing'. - Antonino Fiannaca
GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data. - Ziyang Tang, Xiang Liu, Zuotian Li, Tonglin Zhang, Baijian Yang
SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment. - Correction to: An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning.
- Chao Xu
3D-SMGE: a pipeline for scaffold-based molecular generation and evaluation. - Jie Pan
PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network. - Kai Jing, Yewen Xu, Yang Yang
ScSmOP: a universal computational pipeline for single-cell single-molecule multiomics data analysis. - Ying Wang
Cracking the pattern of tumor evolution based on single-cell copy number alterations. - Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang
Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations. - Ran Zhang
HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction. - Haoran Sun
MMiKG: a knowledge graph-based platform for path mining of microbiota-mental diseases interactions. - Haonan Wu, Jiyun Han, Shizhuo Zhang, Xin Gao, Chaozhou Mou, Juntao Liu
Spatom: a graph neural network for structure-based protein-protein interaction site prediction. - Mingguang Shi
Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data. - Guishan Zhang
Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities. - Maryam Yassi, Aniruddha Chatterjee, Matthew Parry
Application of deep learning in cancer epigenetics through DNA methylation analysis. - Zhongshen Li, Junru Jin
CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides. - Yue Jiang, Xuejiao Hu, Shu Fan, Weijiang Liu, Jingjing Chen, Liang Wang, Qianyun Deng, Jing Yang, Aimei Yang, Zheng Lou, Yuanlin Guan, Han Xia, Bing Gu:
RVFScan predicts virulence factor genes and hypervirulence of the clinical metagenome. - Chao Zhang, Zhi-Wei Duan, Yun-Pei Xu, Jin Liu, Hong-Dong Li:
FEED: a feature selection method based on gene expression decomposition for single cell clustering. - Qiang Su
CLT-seq as a universal homopolymer-sequencing concept reveals poly(A)-tail-tuned ncRNA regulation. - Xin Dai, Longlong Wu
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps. - Chi Zhang, Yiliang Zhang
Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies. - Mark Ziemann
The five pillars of computational reproducibility: bioinformatics and beyond. - Michal Kubacki, Mahesan Niranjan:
Quantum annealing-based clustering of single cell RNA-seq data. - Tiago O. Pereira
Enhancing reinforcement learning for de novo molecular design applying self-attention mechanisms. - Zihan Liu, Jiaqi Wang, Yun Luo, Shuang Zhao, Wenbin Li, Stan Z. Li:
Efficient prediction of peptide self-assembly through sequential and graphical encoding. - Abbas Shojaee, Shao-Shan Carol Huang
Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions. - Fuyi Li
ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction. - Ruifen Cao, Weiling Hu, Pi-Jing Wei, Yun Ding, Yannan Bin
FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses. - Zhe Li, Xinyi Tu, Yuping Chen, Wenbin Lin
HetDDI: a pre-trained heterogeneous graph neural network model for drug-drug interaction prediction. - Hongxin Xiang
Chemical structure-aware molecular image representation learning. - Correction to: CReSIL: accurate identification of extrachromosomal circular DNA from long-read sequences.
- Zihan Zheng
Screening single-cell trajectories via continuity assessments for cell transition potential. - Chi Xiao, Jun Wang, Shenrong Yang, Minxin Heng, Junyi Su, Hao Xiao, Jingdong Song, Weifu Li:
VISN: virus instance segmentation network for TEM images using deep attention transformer. - Anna Ketteler, David B. Blumenthal:
Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer. - Jingjing Zhang, Huiling Zhang, Zhen Ju, Yin Peng, Yi Pan
JCcirc: circRNA full-length sequence assembly through integrated junction contigs. - Bo Li, Chen Peng, Zeran You, Xiaolong Zhang, Shihua Zhang:
Single-cell RNA-sequencing data clustering using variational graph attention auto-encoder with self-supervised leaning. - Xiaoyi Liu, Hongpeng Yang, Chengwei Ai, Yijie Ding, Fei Guo, Jijun Tang:
MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference. - Wen Tao
Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning. - Tao Wang
scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks. - Guo Mao
Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks. - Mehrsa Mardikoraem, Zirui Wang, Nathaniel Pascual, Daniel R. Woldring
Generative models for protein sequence modeling: recent advances and future directions. - Chunwei Ma, Russ Wolfinger:
A prediction model for blood-brain barrier penetrating peptides based on masked peptide transformers with dynamic routing. - Qizhi Pei
Breaking the barriers of data scarcity in drug-target affinity prediction. - Kathi Zarnack, Eduardo Eyras
'Artificial intelligence and machine learning in RNA biology'. - Quanbao Zhang, Lei Cao, Hongtao Song, Kui Lin, Erli Pang:
MkcDBGAS: a reference-free approach to identify comprehensive alternative splicing events in a transcriptome. - Lei Zhang, Sheng Wang, Jie Hou, Dong Si, Junyong Zhu, Renzhi Cao:
ComplexQA: a deep graph learning approach for protein complex structure assessment. - Siddharth Sinha, Jiaheng Li, Benjamin Tam
Classification of PTEN missense VUS through exascale simulations. - Yang Cao
MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection. - Gang Xu, Zhenwei Luo, Ruhong Zhou, Qinghua Wang, Jianpeng Ma:
OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow. - Xiaoshuai Zhang, Huannan Guo, Fan Zhang, Xuan Wang, Kaitao Wu, Shizheng Qiu
HNetGO: protein function prediction via heterogeneous network transformer. - Jiaying Zhao, Chi-Wing Wong, Wai-Ki Ching, Xiaoqing Cheng:
NG-SEM: an effective non-Gaussian structural equation modeling framework for gene regulatory network inference from single-cell RNA-seq data. - Dongmei Ai, Lulu Chen, Jiemin Xie, Longwei Cheng, Fang Zhang, Yihui Luan, Yang Li, Shengwei Hou, Fengzhu Sun, Li Charlie Xia
Identifying local associations in biological time series: algorithms, statistical significance, and applications. - Jing Liang, Zong-Wei Li, Ze-Ning Sun, Ying Bi, Han Cheng
Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction. - Biaoshun Li, Mujie Lin, Tiegen Chen, Ling Wang
FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction. - Hoon Je Seong, Jin Ju Kim, Woo Jun Sul:
ACR: metagenome-assembled prokaryotic and eukaryotic genome refinement tool. - Yuxing Lu
Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes. - Rahul Brahma
KinScan: AI-based rapid profiling of activity across the kinome. - Pourya Naderi Yeganeh, Yue Y. Teo, Dimitra Karagkouni, Yered Pita-Juárez, Sarah L. Morgan, Frank J. Slack, Ioannis S. Vlachos, Winston A. Hide
PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs. - Jiangyi Shao, Qi Zhang, Ke Yan
PreHom-PCLM: protein remote homology detection by combing motifs and protein cubic language model. - Hajar Saihi, Conrad Bessant
Automated and reproducible cell identification in mass cytometry using neural networks. - Tuoyu Liu
Protein-protein interaction and site prediction using transfer learning. - Wei Li, Bin Xiang, Fan Yang, Yu Rong, Yanbin Yin
scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data. - Siyao Wu, Yushan Qiu
ConSpaS: a contrastive learning framework for identifying spatial domains by integrating local and global similarities. - Jiayuan Zhong, Chongyin Han, Pei Chen, Rui Liu
SGAE: single-cell gene association entropy for revealing critical states of cell transitions during embryonic development. - Mingyi Hu, Jinlin Zhu, Guohao Peng, Wenwei Lu, Hongchao Wang, Zhenping Xie:
IMOVNN: incomplete multi-omics data integration variational neural networks for gut microbiome disease prediction and biomarker identification. - Xiaowen Hu, Dayun Liu, Jiaxuan Zhang, Yanhao Fan, Tianxiang Ouyang, Yue Luo, Yuanpeng Zhang, Lei Deng:
A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations. - Jochen Sieg
Searching similar local 3D micro-environments in protein structure databases with MicroMiner. - Yanglan Gan, Wenxiao Liu, Guangwei Xu, Cairong Yan, Guobing Zou:
DMFDDI: deep multimodal fusion for drug-drug interaction prediction. - Yan Cui, Zhikang Wang, Xiaoyu Wang, Yiwen Zhang, Ying Zhang, Tong Pan, Zhe Zhang, Shanshan Li, Yuming Guo, Tatsuya Akutsu, Jiangning Song:
SMG: self-supervised masked graph learning for cancer gene identification. - Jiaojiao Guan, Cheng Peng
PhaGenus: genus-level classification of bacteriophages using a Transformer model. - Sugam Budhraja
Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data. - Fuyu Li, Wenlong Ming, Wenxiang Lu, Ying Wang, Xiaohan Li, Xianjun Dong
FLED: a full-length eccDNA detector for long-reads sequencing data. - Simon Boutry
Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations. - Brandt Bessell, Josh Loecker, Zhongyuan Zhao, Sara Sadat Aghamiri, Sabyasachi Mohanty, Rada Amin, Tomás Helikar
COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.
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