ArticleScientific reports2018
Linking drug target and pathway activation for effective therapy using multi-task learning.
Article in Scientific reports, 2018. 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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration-Response Data.Environmental science & technology · 2026Article
- konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network.Bioinformatics (Oxford, England) · 2023Article
- An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis.BMC biology · 2022Article
- Collectively encoding protein properties enriches protein language models.BMC bioinformatics · 2022Article
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.PloS one · 2022Article
- Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines.Scientific reports · 2021Article
- Discovery of primary prostate cancer biomarkers using cross cancer learning.Scientific reports · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Explainable drug sensitivity prediction through cancer pathway enrichment.Scientific reports · 2021Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.Biomolecules · 2020Article
- Feature selection strategies for drug sensitivity prediction.Scientific reports · 2020Article
- Multitask learning for biomedical named entity recognition with cross-sharing structure.BMC bioinformatics · 2019Article
- Machine learning and feature selection for drug response prediction in precision oncology applications.Biophysical reviews · 2019Review
- A Multi-Task Representation Learning Architecture for Enhanced Graph Classification.Frontiers in neuroscience · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the abundance of large-scale molecular and drug-response data, the insights gained about the mechanisms underlying treatment efficacy in cancer has been in general limited. Machine learning algorithms applied to those datasets most often are used to provide predictions without interpretation, or reveal single drug-gene association and fail to derive robust insights. We propose to use Macau, a bayesian multitask multi-relational algorithm to generalize from individual drugs and genes and explore the interactions between the drug targets and signaling pathways' activation. A typical insight would be: "Activation of pathway Y will confer sensitivity to any drug targeting protein X". We applied our methodology to the Genomics of Drug Sensitivity in Cancer (GDSC) screening, using gene expression of 990 cancer cell lines, activity scores of 11 signaling pathways derived from the tool PROGENy as cell line input and 228 nominal targets for 265 drugs as drug input. These interactions can guide a tissue-specific combination treatment strategy, for example suggesting to modulate a certain pathway to maximize the drug response for a given tissue. We confirmed in literature drug combination strategies derived from our result for brain, skin and stomach tissues. Such an analysis of interactions across tissues might help target discovery, drug repurposing and patient stratification strategies.
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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.