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

Automation scripts and benchmark dataset package for cancer drug prediction deep learning models.

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

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

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

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…

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).

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

Analysis package for 96 well viability analyses