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Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

https://doi.org/10.1371/journal.pone.0061318
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
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Deep learning the sound of boiling for advance prediction of boiling crisis
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A network representation approach for COVID-19 drug recommendation
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An effective multi-task learning framework for drug repurposing based on graph representation learning
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Medical Malpractice and Black-Box Medicine
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Pathway-Guided Deep Neural Network toward Interpretable and Predictive Modeling of Drug Sensitivity
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pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties
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HiDRA: Hierarchical Network for Drug Response Prediction with Attention
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GLA-Synergy: An Interpretable Global-Local Adaptive Framework for Drug Synergy Prediction in Cancer Treatment
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b01008openalex-snapshot
HNet-DNN: Inferring New Drug–Disease Associations with Deep Neural Network Based on Heterogeneous Network Features
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.9b00520openalex-snapshot
Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
inferred← citeshttps://doi.org/10.1021/acschemneuro.5c00649openalex-snapshot
Machine Learning–Based Bioactivity Prediction and Descriptor-Guided Rational Design of Amyloid-β Aggregation Inhibitors
inferred← citeshttps://doi.org/10.1021/acsptsci.2c00212openalex-snapshot
Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics
inferred← citeshttps://doi.org/10.1021/ci500152bopenalex-snapshot
Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization
inferred← citeshttps://doi.org/10.1038/nbt.2877openalex-snapshot
A community effort to assess and improve drug sensitivity prediction algorithms
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Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects
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Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning
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Predicting ligand-dependent tumors from multi-dimensional signaling features
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Synthetic lethality in cancer drug discovery: challenges and opportunities
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Machine learning methods to model multicellular complexity and tissue specificity
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Chemical strategies to overcome resistance against targeted anticancer therapeutics
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Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space
inferred← citeshttps://doi.org/10.1038/s41598-017-10324-yopenalex-snapshot
Automated Classification of Benign and Malignant Proliferative Breast Lesions
inferred← citeshttps://doi.org/10.1038/s41598-018-21622-4openalex-snapshot
A novel heterogeneous network-based method for drug response prediction in cancer cell lines
inferred← citeshttps://doi.org/10.1038/s41598-018-25947-yopenalex-snapshot
Linking drug target and pathway activation for effective therapy using multi-task learning
inferred← citeshttps://doi.org/10.1038/s41598-018-27214-6openalex-snapshot
Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature
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Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer
inferred← citeshttps://doi.org/10.1038/s41598-020-58290-2openalex-snapshot
Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics
inferred← citeshttps://doi.org/10.1038/s41598-020-59656-2openalex-snapshot
Assessment of modelling strategies for drug response prediction in cell lines and xenografts
inferred← citeshttps://doi.org/10.1038/s41598-020-65927-9openalex-snapshot
Feature selection strategies for drug sensitivity prediction
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Ensemble transfer learning for the prediction of anti-cancer drug response
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Explainable drug sensitivity prediction through cancer pathway enrichment
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Network controllability solutions for computational drug repurposing using genetic algorithms
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Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method
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Reliable anti-cancer drug sensitivity prediction and prioritization
inferred← citeshttps://doi.org/10.1038/s41598-024-84711-7openalex-snapshot
Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods
inferred← citeshttps://doi.org/10.1038/s41598-025-16575-4openalex-snapshot
ACLPred: an explainable machine learning and tree-based ensemble model for anticancer ligand prediction
inferred← citeshttps://doi.org/10.1038/s41598-025-17718-3openalex-snapshot
Heterogeneity-optimized method for predicting immune checkpoint blockade response
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Machine learning approaches to drug response prediction: challenges and recent progress
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Metabolic gene alterations impact the clinical aggressiveness and drug responses of 32 human cancers
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Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade
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Learning chemical sensitivity reveals mechanisms of cellular response
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Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine
inferred← citeshttps://doi.org/10.1038/srep31619openalex-snapshot
Multitask learning improves prediction of cancer drug sensitivity
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Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties
inferred← citeshttps://doi.org/10.1039/c4ib00167bopenalex-snapshot
Combinative in vitro studies and computational model to predict 3D cell migration response to drug insult
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Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects
inferred← citeshttps://doi.org/10.1039/c5ib00283dopenalex-snapshot
Time series modeling of live-cell shape dynamics for image-based phenotypic profiling
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Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs
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Data integration and predictive modeling methods for multi-omics datasets
inferred← citeshttps://doi.org/10.1039/c9mo00162jopenalex-snapshot
Modular within and between score for drug response prediction in cancer cell lines
inferred← citeshttps://doi.org/10.1039/d5dd00284bopenalex-snapshot
Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach
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Bioinformatics in translational drug discovery
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Artificial Intelligence: A New Tool in Oncologist's Armamentarium
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Artificial Intelligence: An Introduction to Technical Advancements and Current Biological Applications in Plants
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Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications
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Probing the characteristics and biofunctional effects of disease-affected cells and drug response via machine learning applications
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Virtual screening of chemical compounds active against breast cancer cell lines based on cell cycle modelling, prediction of cytotoxicity and interaction with targets
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Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines
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SERIES: eHealth in primary care. Part 1: Concepts, conditions and challenges
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A new approach to assess drug sensitivity in cells for novel drug discovery
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Artificial intelligence in early drug discovery enabling precision medicine
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Foundations of plasmas for medical applications
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Foundations of machine learning for low-temperature plasmas: methods and case studies
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Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics
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DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration
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A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling
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How much can deep learning improve prediction of the responses to drugs in cancer cell lines?
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Representation of molecules for drug response prediction
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An overview of machine learning methods for monotherapy drug response prediction
inferred← citeshttps://doi.org/10.1093/bib/bbac054openalex-snapshot
Drug repositioning based on multi-view learning with matrix completion
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DeepTTA: a transformer-based model for predicting cancer drug response
inferred← citeshttps://doi.org/10.1093/bib/bbae242openalex-snapshot
A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction
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Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data
inferred← citeshttps://doi.org/10.1093/bib/bbae379openalex-snapshot
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
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Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity
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Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting
showing the first 200 of 535
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