ArticleProceedings of the National Academy of Sciences of the United States of America2018
Training replicable predictors in multiple studies.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer.Cancer biology & therapy · 2021Pooled it
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- Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis.Frontiers in genetics · 2024Article
- glmmPen: High Dimensional Penalized Generalized Linear Mixed Models.The R journal · 2023Article
- Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.PLoS computational biology · 2023Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- A harmonized resource of integrated prostate cancer clinical, -omic, and signature features.Scientific data · 2023Article
- DNA methylation-based biomarkers for cardiometabolic-related traits and their importance for risk stratification.Current opinion in epidemiology and public health · 2023Article
- A pairwise strategy for imputing predictive features when combining multiple datasets.Bioinformatics (Oxford, England) · 2023Article
- Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing.The annals of applied statistics · 2022Article
- Universal adaptability: Target-independent inference that competes with propensity scoring.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Robustifying genomic classifiers to batch effects via ensemble learning.Bioinformatics (Oxford, England) · 2021Article
- Weight-Based Framework for Predictive Modeling of Multiple Databases With Noniterative Communication Without Data Sharing: Privacy-Protecting Analytic Method for Multi-Institutional Studies.JMIR medical informatics · 2021Article
- SomaticCombiner: improving the performance of somatic variant calling based on evaluation tests and a consensus approach.Scientific reports · 2020Article
- Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics.Journal of the American Heart Association · 2020Article
- The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models.Biostatistics (Oxford, England) · 2020Article
- Tree-Weighting for Multi-Study Ensemble Learners.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
This article considers replicability of the performance of predictors across studies. We suggest a general approach to investigating this issue, based on ensembles of prediction models trained on different studies. We quantify how the common practice of training on a single study accounts in part for the observed challenges in replicability of prediction performance. We also investigate whether ensembles of predictors trained on multiple studies can be combined, using unique criteria, to design robust ensemble learners trained upfront to incorporate replicability into different contexts and populations.
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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.