ArticleBioinformatics (Oxford, England)2017
Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 77 citations in OpenAlex.
- Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025Review
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening.Cancers · 2024Article
- AI approaches for the discovery and validation of drug targets.Cambridge prisms. Precision medicine · 2024Review
- Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.Biomedicines · 2021Article
- SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.BMC bioinformatics · 2021Article
- A Methodological Framework to Discover Pharmacogenomic Interactions Based on Random Forests.Genes · 2021Article
- Prediction of Visual Field Progression from OCT Structural Measures in Moderate to Advanced Glaucoma.American journal of ophthalmology · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches.Briefings in bioinformatics · 2021Article
- Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021Review
- Article
- Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.BMC bioinformatics · 2020Article
- Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.Cancer cell · 2020Article
- An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs.Royal Society open science · 2020Article
- ADRML: anticancer drug response prediction using manifold learning.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationA prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations.
resultsWe present a novel approach that leverages on systematic integration of data sources to identify response predictive features of multiple drugs. To solve the modeling task we implement a Bayesian linear regression method. To further improve the usefulness of the proposed model, we exploit the known human cancer kinome for identifying biologically relevant feature combinations. In case studies with a synthetic dataset and two publicly available cancer cell line datasets, we demonstrate the improved accuracy of our method compared to the widely used approaches in drug response analysis. As key examples, our model identifies meaningful combinations of features for the well known EGFR, ALK, PLK and PDGFR inhibitors. AVAILABILITY AND IMPLEMENTATION: The source code of the method is available at https://github.com/suleimank/mvlr . CONTACT: muhammad.ammad-ud-din@helsinki.fi or suleiman.khan@helsinki.fi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.