ArticleNature communications2024
Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Limitations of nomogram models in predicting survival outcomes for glioma patients.Frontiers in immunology · 2025Pooled it
- UDEC-MO: an uncertainty-guided deep embedded clustering framework for bulk and single-cell multi-omics data.Briefings in bioinformatics · 2026Article
- Context-dependent synthetic lethality - an emerging precision therapeutic approach.Nature reviews. Cancer · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Single-cell-marker-based subtyping and multi-level analyses uncover the prognostic effects, dysregulations and therapeutic indicative potential of an eight-gene signature in lung adenocarcinoma.Cancer cell international · 2026Article
- MCVAE-based multi-omic anomaly detection in Fragile X Syndrome.NAR molecular medicine · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Integrative single-cell and spatial transcriptomics with explainable AI reveal lethal prognostic axis in prostate cancer.NPJ digital medicine · 2026Article
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- Plying potency assays for immunotherapy of solid tumors.Frontiers in immunology · 2026Review
- Primer on Modelling Approaches for Omics Data.Advances in experimental medicine and biology · 2026Article
- Recent advances in omics-based research of mitral valve disease.Frontiers in cardiovascular medicine · 2026Review
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Context-aware gene dependency modeling via graph attention networks for precision oncology.Journal of translational medicine · 2025Article
- Interpretable machine learning analysis of clinicopathological and immunonutritional biomarkers for predicting lymph node metastasis in gastric cancer.Scientific reports · 2025Article
- Drug resistance in cancer: molecular mechanisms and emerging treatment strategies.Molecular biomedicine · 2025Review
- Integrative Omics and AI-Driven Systems Biology: Multilayer Networks DecodingJournal of proteome research · 2025Review
- A quantitative study of pathologists' perceptions towards artificial intelligence-assisted diagnostic system.PLOS digital health · 2025Article
- Federated Deep Learning Enables Cancer Subtyping by Proteomics.Cancer discovery · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOSA (Multi-Omic Synthetic Augmentation), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.7% in the number of multi-omic profiles and thereby generating a complete DepMap for 1523 cancer cell lines. The synthetically enhanced data increases statistical power, uncovering less studied mechanisms associated with drug resistance, and refines the identification of genetic associations and clustering of cancer cell lines. By applying SHapley Additive exPlanations (SHAP) for model interpretation, MOSA reveals multi-omic features essential for cell clustering and biomarker identification related to drug and gene dependencies. This understanding is crucial for developing much-needed effective strategies to prioritize cancer targets.
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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.