ArticleBMC bioinformatics2020
Leveraging TCGA gene expression data to build predictive models for cancer drug response.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis.BMC geriatrics · 2023Pooled it
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- DrugSurvPlot: A Novel Web-Based Platform Harnessing Drug Sensitivity Scores as Molecular Biomarkers for Pan-Cancer Survival Prognosis.Current gene therapy · 2026Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Harmonized nonhuman cancer database.bioRxiv : the preprint server for biology · 2025Article
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- GEPIA3: Enhanced drug sensitivity and interaction network analysis for cancer research.Nucleic acids research · 2025Article
- A disentangled generative model for improved drug response prediction in patients via sample synthesis.Journal of pharmaceutical analysis · 2025Article
- Identification of Cuproptosis-Associated Prognostic Gene Expression Signatures from 20 Tumor Types.Biology · 2024Article
- Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology.Journal of proteome research · 2024Review
- Review
- Survival Rate and Chronic Diseases of TCGA Cancer and KoGES Normal Samples by Clustering for DNA Methylation.Life (Basel, Switzerland) · 2024Article
- Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma.Heliyon · 2024Article
- Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer.Health data science · 2024Article
- NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.BMC medicine · 2022Article
- A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening.Nature machine intelligence · 2022Article
- Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers.Matrix biology plus · 2022Article
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer.Frontiers in medicine · 2022Review
- A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data.Journal of oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine.
resultsWe focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis.
conclusionsPrimary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.
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Registered trials
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.