ArticleBMC bioinformatics2021
SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.iScience · 2026Article
- Context defines precision: rethinking KRAS inhibition in oncology.NPJ precision oncology · 2026Review
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 2025Article
- An attention-based mRNA transformer network for accurate prediction of melanoma response to immune checkpoint inhibitors.Scientific reports · 2025Article
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- The specification game: rethinking the evaluation of drug response prediction for precision oncology.Journal of cheminformatics · 2025Article
- SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction.bioRxiv : the preprint server for biology · 2025Article
- Drug discovery and mechanism prediction with explainable graph neural networks.Scientific reports · 2025Article
- A versatile attention-based neural network for chemical perturbation analysis and its potential to aid surgical treatment: an experimental study.International journal of surgery (London, England) · 2024Article
- Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024Review
- Learning chemical sensitivity reveals mechanisms of cellular response.Communications biology · 2024Article
- Cancer drug sensitivity estimation using modular deep Graph Neural Networks.NAR genomics and bioinformatics · 2024Article
- Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.Journal of chemical information and modeling · 2024Article
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Optimal fusion of genotype and drug embeddings in predicting cancer drug response.Briefings in bioinformatics · 2024Article
- Response score-based protein structure analysis for cancer prediction aided by the Internet of Things.Scientific reports · 2024Article
- Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundOne of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model.
resultsWe presented here a novel deep-learning model that integrates gene expression, genetic mutation, and chemical structure of compounds in a multi-task convolutional architecture. We applied our model to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. We selected relevant cancer-related genes based on oncology genetics database and L1000 landmark genes, and used their expression and mutations as genomic features in model training. We obtain the cheminformatics features for compounds from PubChem or ChEMBL. Our finding is that combining gene expression, genetic mutation, and cheminformatics features greatly enhances the predictive performance.
conclusionWe implemented an extended Graph Neural Network for molecular graphs and Convolutional Neural Network for gene features. With the employment of multi-tasking and self-attention functions to monitor the similarity between compounds, our model outperforms recently published methods using the same training and testing datasets.
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