ArticlePLoS computational biology2023
A subcomponent-guided deep learning method for interpretable cancer drug response prediction.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Multiscale higher-order molecular simplicial complex embedding for drug response prediction.Bioinformatics (Oxford, England) · 2026Article
- Topology-Aware Deep Learning on Higher-Order Structures for Drug Response Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- RLASON-CDR: a reinforcement learning-driven adaptive synergistic optimization network for cancer drug response prediction.Briefings in bioinformatics · 2026Article
- drGT: interpretable drug response prediction with attention-guided gene attribution on a drug-cell-gene heterogeneous graph.BMC bioinformatics · 2026Article
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer.Cell reports methods · 2026Article
- BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.BMC bioinformatics · 2026Article
- DistRMI: a deep distance-aware neural network for explainable RNA loop motif-small molecule interaction prediction.Briefings in bioinformatics · 2025Article
- drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network.ArXiv · 2025Article
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes.iScience · 2025Article
- Cancer drug sensitivity estimation using modular deep Graph Neural Networks.NAR genomics and bioinformatics · 2024Article
- Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization.NPJ precision oncology · 2024Article
- Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.Frontiers in genetics · 2024Article
- MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.Bioinformatics (Oxford, England) · 2023Article
- A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.PLoS computational biology · 2023Article
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2 authors.
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Abstract
Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes may primarily attribute to a few finer-level 'subcomponents', such as privileged substructures of the drug or gene signatures of the cancer cell, thus producing predictions that are hard to explain. Herein, we present SubCDR, a subcomponent-guided deep learning method for interpretable CDR prediction, to recognize the most relevant subcomponents driving response outcomes. Technically, SubCDR is built upon a line of deep neural networks that enables a set of functional subcomponents to be extracted from each drug and cell line profile, and breaks the CDR prediction down to identifying pairwise interactions between subcomponents. Such a subcomponent interaction form can offer a traceable path to explicitly indicate which subcomponents contribute more to the response outcome. We verify the superiority of SubCDR over state-of-the-art CDR prediction methods through extensive computational experiments on the GDSC dataset. Crucially, we found many predicted cases that demonstrate the strength of SubCDR in finding the key subcomponents driving responses and exploiting these subcomponents to discover new therapeutic drugs. These results suggest that SubCDR will be highly useful for biomedical researchers, particularly in anti-cancer drug design.
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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.