ReviewBiophysical reviews2019
Machine learning and feature selection for drug response prediction in precision oncology applications.
Review in Biophysical reviews, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
91 citing papers in PubMed.
- SADRP: A structurally aware multi-modal framework for drug response prediction.PLoS computational biology · 2026Article
- Aumolertinib in non-small cell lung cancer with uncommon EGFR exon 19 deletions: a real-world dual-center study.BMC cancer · 2026Observational
- Machine learning-based individualized prediction: risk assessment of retinopathy in preterm infants at high altitude.BMC ophthalmology · 2026Article
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Oncolytic viruses: advanced strategies in cancer therapy.Signal transduction and targeted therapy · 2026Review
- Precision medicine in rheumatoid arthritis: advances and clinical applications of multi-omics biomarkers.Frontiers in immunology · 2026Review
- Microenvironmental signals combine to induce non-additive molecular and phenotypic responses in mammary epithelial cells.iScience · 2025Article
- Role of intratumoral heterogeneity in metastatic progression and drug resistance.Discover oncology · 2025Review
- Revolutionizing multi-omics analysis with artificial intelligence and data processing.Quantitative biology (Beijing, China) · 2025Review
- Zebrafish metabolomics: a comprehensive approach to understanding health and disease.Functional & integrative genomics · 2025Review
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.Journal of medical systems · 2025Article
- Computational frameworks transform antagonism to synergy in optimizing combination therapies.NPJ digital medicine · 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
- Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model.Lipids in health and disease · 2024Article
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- Hybrid deep learning technique for COX-2 inhibition bioactivity detection against breast cancer disease.Biomedical engineering letters · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy.Scientific reports · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input "big data" require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal. We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional multi-omics profiles along with knowledge about cancer pathways targeted by anti-cancer compounds when predicting their phenotypic responses.
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What OpenQuestion holds
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.