A computational method to rank and infer drug-responsive cell population towards in-silico drug perturbation using a target-perturbed gene regulatory network (tpGRN) for single-cell transcriptomic data

Pipeline for testing drug response prediction models in a statistically and biologically sound way.

Dataset package for facile training and testing of machine learning/AI algorithms that predict drug response in cancer model systems.

A general Python framework for using hidden Markov models on binary trees or cell lineage trees.

Sobolev alignment of deep probabilistic models for comparing single cell profiles

Python implementation of TRANSACT, a tool to transfer non-linear predictors of drug response from model systems to tumors.

A collection of source codes for network-based multi-omics analysis using integrated genome-wide association studies (GWAS) and transcriptomic data to identify genetic contribution into lithium response in patients with bipolar disorder (BD).

DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response

This repository contains code for MFmap (model fidelity map), a semi-supervised generative model integrating gene expression, copy number and mutation data, matching cell lines to cancer subtypes. MFmap compresses high dimensional omics data of cell lines and bulk tumours into subtype informative low dimensional latent representations and predic…

This repository is for ER+ breast cancer scRNA-seq data processing and figure generation.

Analysis package for 96 well viability analyses

Interpretable drug-response GNN with a pathway/TF concept-bottleneck; built for cross-panel generalization and conserved-subnetwork discovery.

💊 Predict drug responses using multi-omics data with this advanced platform, enhancing precision medicine in oncology through effective computational analysis.