ArticleBMC bioinformatics2018
Investigation of model stacking for drug sensitivity prediction.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Development and validation of a machine learning model to predict early recurrence after surgery in NSCLC patients.Scientific reports · 2025Article
- A Comparison of AI and Population PK Models to Predict the Concentrations of Antiepileptic Drugs Using Therapeutic Drug Monitoring Records.Clinical and translational science · 2025Article
- Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification.Scientific reports · 2025Article
- DeepDR: a deep learning library for drug response prediction.Bioinformatics (Oxford, England) · 2024Article
- Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.Briefings in bioinformatics · 2024Article
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- Reliable anti-cancer drug sensitivity prediction and prioritization.Scientific reports · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- Estimation of cognitive impairment in chronic pain patients and characteristics of estimated mild cognitive impairment.Frontiers in neurology · 2024Article
- Article
- A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling.BMC chemistry · 2023Article
- Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy.Molecules (Basel, Switzerland) · 2023Article
- Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method.Scientific reports · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction.Bioinformatics (Oxford, England) · 2021Article
- An Ensemble Model for Prediction of Vancomycin Trough Concentrations in Pediatric Patients.Drug design, development and therapy · 2021Article
- Deep learning of pharmacogenomics resources: moving towards precision oncology.Briefings in bioinformatics · 2020Article
- Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination.Frontiers in neurology · 2020Article
- Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.Molecular therapy. Nucleic acids · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundA significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types.
resultsWe explore the predictive performance of model stacking and the effect of stacking on the predictive bias and squared error. In addition we discuss the analytical underpinnings supporting the advantages of stacking in reducing squared error and inherent bias of random forests in prediction of outliers. The framework is tested on a setup including gene expression, drug target, physical properties and drug response information for a set of drugs and cell lines.
conclusionThe performance of individual and stacked models are compared. We note that stacking models built on two heterogeneous datasets provide superior performance to stacking different models built on the same dataset. It is also noted that stacking provides a noticeable reduction in the bias of our predictors when the dominant eigenvalue of the principle axis of variation in the residuals is significantly higher than the remaining eigenvalues.
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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.