ArticleBMC bioinformatics2018
Feature selection for high-dimensional temporal data.
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 13 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.
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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.
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Who cites it
13 citing papers in PubMed.
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.JMIR medical informatics · 2025Article
- Development and evaluation of a chronic kidney disease risk prediction model using random forest.Frontiers in genetics · 2024Article
- Identification of key biomarkers for STAD using filter feature selection approaches.Scientific reports · 2022Article
- Bayesian regularization for a nonstationary Gaussian linear mixed effects model.Statistics in medicine · 2022Article
- Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview.Environmental epidemiology (Philadelphia, Pa.) · 2021Review
- Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles.BMC bioinformatics · 2021Article
- Metaheuristic approaches in biopharmaceutical process development data analysis.Bioprocess and biosystems engineering · 2019Review
- Elastic net-based prediction of IFN-β treatment response of patients with multiple sclerosis using time series microarray gene expression profiles.Scientific reports · 2019Article
- Scanning of Genetic Variants and Genetic Mapping of Phenotypic Traits in Gilthead Sea Bream Through ddRAD Sequencing.Frontiers in genetics · 2019Article
- Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control.PloS one · 2019Article
- A greedy feature selection algorithm for Big Data of high dimensionality.Machine learning · 2019Article
- Feature selection with the R packageF1000ResearchArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundFeature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional "omics" temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables.
resultsThe algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics.
conclusionsThe use of this algorithm is suggested for variable selection with high-dimensional temporal data.
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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.