ArticleScientific reports2021
Explainable drug sensitivity prediction through cancer pathway enrichment.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- Drug response profile-based machine learning enables strategic cell line and compound selection for drug development.Bioinformatics (Oxford, England) · 2026Article
- AMDRP: adaptive drug feature fusion and multihead bidirectional cross-attention network for drug-cancer cell response prediction.Molecular diversity · 2026Article
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- A Holistic Review of Oncological Drug Targets and Trajectories of Resistance in Cancer Therapy.Oncology research · 2026Review
- Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens.PloS one · 2026Article
- Leveraging uncertainty estimates for drug response prediction in cancer cell lines.Bioinformatics advances · 2026Article
- A novel explainable AI for revealing determinants of cancer drug response through integrative multi-omics analysis.Frontiers in oncology · 2026Article
- AdaSemb: an adaptive knowledge-driven deep learning framework integrating cancer protein assemblies for predicting PI3Kα inhibitor response and resistance.Briefings in bioinformatics · 2025Article
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- CellHit: a web server to predict and analyze cancer patients' drug responsiveness.Nucleic acids research · 2025Article
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- Gene Signatures and Oncology Treatment Implications.Hematology/oncology clinics of North America · 2025Review
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- The specification game: rethinking the evaluation of drug response prediction for precision oncology.Journal of cheminformatics · 2025Article
- Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.PLoS computational biology · 2025Article
- Learning and actioning general principles of cancer cell drug sensitivity.Nature communications · 2025Article
- The Hallmarks of Predictive Oncology.Cancer discovery · 2025Article
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
- Unsupervised cell line embedding using pairwise drug response correlation.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose PathDSP: a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer dataset. Using a deep neural network, we outperform state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.
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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.