ArticlePloS one2015
DISIS: prediction of drug response through an iterative sure independence screening.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed, 29 citations in OpenAlex.
- Text-mining-based feature selection for anticancer drug response prediction.Bioinformatics advances · 2024Article
- Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.ACS pharmacology & translational science · 2023Article
- Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.Frontiers in molecular biosciences · 2023Article
- Dissecting the Genome for Drug Response Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.Biomedicines · 2021Article
- Iterative sure independent ranking and screening for drug response prediction.BMC medical informatics and decision making · 2020Article
- An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression.Molecular therapy. Nucleic acids · 2020Article
- Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model.Cancers · 2019Article
- Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data.Frontiers in chemistry · 2019Article
- A quantile regression forest based method to predict drug response and assess prediction reliability.PloS one · 2018Article
- High-Resolution Genetics Identifies the Lipid Transfer Protein Sec14p as Target for Antifungal Ergolines.PLoS genetics · 2016Article
- Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data.Computational and mathematical methods in medicine · 2016Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.
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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.