ArticleNPJ precision oncology2024
Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.Frontiers in pharmacology · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Due to cancer's complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patient makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods' abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanisms of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balanced reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.
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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.