ArticleFrontiers in genetics2018
Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner.iScience · 2026Article
- Genetic Determinants of T-Cell Homeostasis in Critical Illness: An Exploratory Analysis of Immune Gene Variants and TREC Dynamics.Journal of personalized medicine · 2026Article
- Machine learning approaches for biomarker discovery using single-cell RNA sequencing.Frontiers in bioinformatics · 2026Review
- Machine learning reveals distinct gene expression signatures across tissue states in stony coral tissue loss disease.Royal Society open science · 2025Article
- Generative prediction of causal gene sets responsible for complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.Briefings in bioinformatics · 2024Article
- Article
- Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.Nature computational science · 2023Article
- Multimodal AI for prediction of distant metastasis in carcinoma patients.Frontiers in bioinformatics · 2023Article
- Transcriptomics indicate nuclear division and cell adhesion not recapitulated in MCF7 and MCF10A compared to luminal A breast tumours.Scientific reports · 2022Article
- Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.Chemical research in toxicology · 2022Review
- Prediction of lung cancer using gene expression and deep learning with KL divergence gene selection.BMC bioinformatics · 2022Article
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- Angiogenesis goes computational - The future way forward to discover new angiogenic targets?Computational and structural biotechnology journal · 2022Review
- The Key Factors Predicting Dementia in Individuals With Alzheimer's Disease-Type Pathology.Frontiers in aging neuroscience · 2022Article
- The acute phase protein lactoferrin is a key feature of Alzheimer's disease and predictor of Aβ burden through induction of APP amyloidogenic processing.Molecular psychiatry · 2021Article
- Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods.BMC medical genomics · 2021Article
- Article
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2 authors.
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Abstract
Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes of interest in a particular study setting can thus be a daunting task, usually relying on automated computational methods. Moreover, most biological questions imply that such a search should be performed in a multivariate setting, to take into account the inter-genes relationships. Differential expression analysis commonly yields large lists of genes deemed significant, even after adjustment for multiple testing, making the subsequent study possibilities extensive. Here, we explore the use of supervised learning methods to rank large ensembles of genes defined by their expression values measured with RNA-Seq in a typical 2 classes sample set. First, we use one of the variable importance measures generated by the random forests classification algorithm as a metric to rank genes. Second, we define the EPS (extreme pseudo-samples) pipeline, making use of VAEs (Variational Autoencoders) and regressors to extract a ranking of genes while leveraging the feature space of both virtual and comparable samples. We show that, on 12 cancer RNA-Seq data sets ranging from 323 to 1,210 samples, using either a random forests-based gene selection method or the EPS pipeline outperforms differential expression analysis for 9 and 8 out of the 12 datasets respectively, in terms of identifying subsets of genes associated with survival. These results demonstrate the potential of supervised learning-based gene selection methods in RNA-Seq studies and highlight the need to use such multivariate gene selection methods alongside the widely used differential expression analysis.
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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.