depmap

What you need to process the Quarterly DepMap-Omics releases from Terra

Cancer Dependency Map package

CanDI - A global cancer data integrator

Falsification-first research on cancer genetic vulnerabilities that replicate across independent CRISPR screens.

An integrative resource for deubiquitinating enzymes (DUBs) served at https://labsyspharm.github.io/dubportal

Package to predict dependencies between cell lines and genes using Network Representation Learning (NRL) based Link Prediction

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

Multi-omics kinase target prioritization pipeline for triple-negative breast cancer (TNBC) — CTS scoring across 90 real RTK/NRTK kinases.

multi-omic CTS extension and dependency-prediction ML model

Multi-omic computational pipeline for prioritizing combination-therapy hypotheses in triple-negative breast cancer — kinase target scoring (CTS), regimen ranking (MDCOE/HCOS), DepMap/CPTAC validation, agentic literature discovery, and an in-development GNN-based drug-synergy predictor.

ML pipeline linking cancer cell-line metabolomics to drug response (DepMap + GDSC).

A signed readout of organelle dynamics — biogenesis minus selective clearance — from ordinary expression data. Predicts mitochondrial drug and genetic vulnerability across 1,066 cancer cell lines.

Digital patient generation and drug response prediction via TCGA-DepMap integration — CVAE virtual patients, multi-level similarity scoring, 578-compound drug profiling

Data from the Broad's Project Achilles synthetic-lethal screen ready for use in R.

Contradiction-aware cancer gene hypothesis triage app using public-data evidence layers.

A functional genomics framework for classifying homologous recombination deficiency and mapping PARP inhibitor sensitivity across DepMap cancer cell lines.

Predicts CRISPR gene dependencies across 1,066 DepMap cancer models and ranks selective therapeutic hypotheses. Precision@10 0.965 on held-out models, validated against an independent Sanger screen.

Evidence-integrating pipeline that nominates novel, druggable small-molecule cancer targets from DepMap dependency, synthetic lethality, single-cell specificity, safety, and tractability — with an LLM nomination ensemble and a gene-masking bias control. Validated on glioblastoma.

Machine learning pipeline for identifying ivermectin-associated biomarkers and drug repurposing opportunities across human cancers.

Interactive web viewer for TF ChIP-seq binding programs at canonical protein-coding TSSs (Ensembl GRCh38.114). Streamlit + Plotly + DuckDB; ~1,300 TFs × ~19,700 TSSs; NMF programs/archetypes, GTEx + DepMap overlays, atlas-wide TF×TF network.