ArticleScientific reports2017
Drug Response Prediction as a Link Prediction Problem.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- RLASON-CDR: a reinforcement learning-driven adaptive synergistic optimization network for cancer drug response prediction.Briefings in bioinformatics · 2026Article
- Bias-aware training and evaluation of link prediction algorithms in network biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- BANDRP: a bilinear attention network for anti-cancer drug response prediction based on fingerprint and multi-omics.Briefings in bioinformatics · 2024Article
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms.Frontiers in molecular biosciences · 2024Article
- MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.Bioinformatics (Oxford, England) · 2023Article
- A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.Briefings in bioinformatics · 2023Review
- A subcomponent-guided deep learning method for interpretable cancer drug response prediction.PLoS computational biology · 2023Article
- Precision oncology: a review to assess interpretability in several explainable methods.Briefings in bioinformatics · 2023Review
- Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach.JMIR formative research · 2023Article
- Drug response prediction using graph representation learning and Laplacian feature selection.BMC bioinformatics · 2022Article
- DrDimont: explainable drug response prediction from differential analysis of multi-omics networks.Bioinformatics (Oxford, England) · 2022Article
- Extracting functional insights from loss-of-function screens using deep link prediction.Cell reports methods · 2022Article
- Machine learning for multi-omics data integration in cancer.iScience · 2022Review
- Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods.International journal of molecular sciences · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach.PloS one · 2022Article
- A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer.Frontiers in medicine · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute "network profiles" for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles.
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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.