Single-cell perturbation analysis

A reproducible computational pipeline for processing and analyzing single-cell RNA-seq data with CRISPR perturbations, designed for the Virtual Cell Challenge 2025. Features automated quality control, normalization, class balancing, and batch integration using Snakemake.

A Python simulator for modeling gene expression perturbations using a recursive, depth-limited path summation model.

Macros and functions to work with DSGE models.

perturbation of coupled model input over a space of input variables

Symbolic calculation of the (van Vleck) Floquet recursion formula

Single-cell Analysis of Perturbational Effects using Machine Learning

compact: co-expression module perturbation analysis

Mandelbrot Generator based on perturbation and series approximation

MSAffect is a computational pipeline to evaluate the robustness of AlphaFold2 protein structure predictions under adversarial MSA perturbations to identify structural sensitivity and confidence shifts in neural-network-based folding.

M0dular Action System & Interface, Custom AI/AGENT Framework (made by a non-coder)

DataArmor is a cutting-edge tool focused on safeguarding privacy in today's data-driven world using K-anonymity L-diversity and t-closeness privacy model. As the sharing of personal and microdata grows, ensuring the protection of individual identities during data publication and analysis becomes essential.

Symbolic Perturbation Theory (SymPT) is a Python package for symbolic perturbative transformations on quantum systems. SymPT helps compute effective Hamiltonians for both time-independent and time-dependent systems at both operator and matrix level.

Sophisticated astrodynamics and space mission simulator. Calculate n-body trajectories, perform orbital maneuvers, apply various perturbations, generate plots, and more! Minimal dependencies.

scPerturb: A resource and a python/R tool for single-cell perturbation data

Exploring perturbation stategies to improve LIME explanations stability.

Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.

Interpretable Explanations of Black Boxes by Meaningful Perturbation Pytorch

Python implementation of a data perturbation method to determine relevant features for NN learning

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.