A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.
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Updated
Sep 22, 2026 - Python
A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.
A Full Stack Variant Effect Predictor leveraging the Evo2 LLM for Single Nucleotide Variant(SNV) pathogenicity analysis, powered by a Modal‑deployed FastAPI GPU backend and a Next.js + Shadcn UI frontend
A deep learning model (EVO2-500M) for predicting host specificity of eukaryote-infecting viruses CDNA sequence
a web app that can classify how likely specific mutations in DNA are to cause diseases (variant effect prediction). We will deploy and use the state-of-the-art Evo2 large language model, and use it to predict the pathogenicity of single nucleotide variants (SNVs)
Evo 2 の内部表現をスパースオートエンコーダで読むハンズオン
Genome analysis toolkit powered by Evo foundation models.
Native Apple Silicon support for Arc Institute's Evo2. Run StripedHyena models locally on macOS via Metal Performance Shaders.
?? Ultron Workflow - Where Science Meets Sarcasm ??? | Visual workflows, protein & DNA labs, drug discovery, 3-D mech/circuit, all powered by Ultron - your brilliantly sarcastic superintelligence. He roasts, then results. ????
Unofficial PyTorch and Transformers port of Arc Institute Evo 2 for DNA modeling and generation. Loads official checkpoints with no Vortex or custom kernels needed.
AI-powered variant pathogenicity prediction using the Evo2 genomic language model on GPU, cross-validated against ClinVar.
This project critically analyzes the data pipeline of EVO 2, a cutting-edge AI model for genomic research developed by the Arc Institute and collaborators. The focus lies in evaluating its risks, ethical challenges, and governance structures in the context of data privacy, genome editing, and AI-driven biomedical innovation.
Open standard for DNA-based information archival
AI-powered variant pathogenicity prediction for cancer genes using Evo2-7B evolutionary foundation model. Combines deep learning, VEP annotation, gnomAD frequencies, and explainable AI for precision oncology. Built with Next.js + Modal + PostgreSQL.
Exploratory Evo 2 promoter-grammar analysis with frozen aggregate statistics and figures for human protein-coding genes.
Prompt-conditioned generation and LoRA finetuning of Evo 2 on rbcL, with a reproducible evaluation harness
Native Windows app that runs the Evo 2 genomic language model on a GPU in your own Google Cloud project and returns per-base Shannon entropy tracks for IGV, Geneious, SnapGene and Benchling.
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