ArticleScience advances2021
Predicting and characterizing a cancer dependency map of tumors with deep learning.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.iScience · 2026Article
- Graph in Graph (GiG): A novel graph AI framework for integrating and interpreting medical and omics data.bioRxiv : the preprint server for biology · 2026Article
- Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.Genetic epidemiology · 2026Article
- Context-dependent synthetic lethality - an emerging precision therapeutic approach.Nature reviews. Cancer · 2026Review
- Co-repression of Yap1 and Sox9 abrogates established cholangiocarcinoma by eliminating transcriptional compensation.Clinical and molecular hepatology · 2026Article
- Unbiased CRISPR synthetic lethal screening for genetic vulnerabilities in a succinate dehydrogenase-loss model of paraganglioma.iScience · 2026Article
- Predicting gene essentiality and drug response from preclinical perturbation screens with layered ensemble of autoencoders and predictors.Scientific reports · 2026Article
- Article
- Co-repression ofbioRxiv : the preprint server for biology · 2026Article
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- Metformin sensitizes non-small cell lung cancer to CDK4/6 inhibitor treatment by reducing lysosomal trapping.Science China. Life sciences · 2026Article
- Challenges and opportunities for oncology drug repurposing informed by synthetic lethality.NPJ systems biology and applications · 2025Article
- Context-aware gene dependency modeling via graph attention networks for precision oncology.Journal of translational medicine · 2025Article
- Survival Prediction in Stomach Cancer with Deep Learning: Unveiling Model Decisions with LIME and SHAP.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Explainable deep learning for identifying cancer driver genes based on the Cancer Dependency Map.bioRxiv : the preprint server for biology · 2025Article
- High-Throughput Empirical and Virtual Screening To Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.Analytical chemistry · 2025Article
- Integration of Bulk and Single-Cell Transcriptomics Reveals BCL2L14 as a Novel IGKC+ T Cell-Associated Therapeutic Target in Breast Cancer.Journal of inflammation research · 2025Article
- Multimodal deep learning approaches for precision oncology: a comprehensive review.Briefings in bioinformatics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines and further to tumors. Here, we present DeepDEP, a deep learning model that predicts cancer dependencies using integrative genomic profiles. It uses a unique unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. We demonstrated DeepDEP's improvement over conventional machine learning methods and validated the performance with three independent datasets. By systematic model interpretations, we extended the current dependency maps with functional characterizations of dependencies and a proof-of-concept in silico assay of synthetic essentiality. We applied DeepDEP to pan-cancer tumor genomics and built the first pan-cancer synthetic dependency map of 8000 tumors with clinical relevance. In summary, DeepDEP is a novel tool for investigating cancer dependency with rapidly growing genomic resources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.