ArticlePloS one2014
A robust and accurate method for feature selection and prioritization from multi-class OMICs data.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults.European journal of epidemiology · 2024Article
- Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection.Journal of translational medicine · 2024Article
- Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.Frontiers in digital health · 2023Article
- Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm.Cancers · 2022Article
- Nextcast: A software suite to analyse and model toxicogenomics data.Computational and structural biotechnology journal · 2022Article
- Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials.Scientific data · 2021Article
- Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.Respiratory physiology & neurobiology · 2021Article
- A random forest based biomarker discovery and power analysis framework for diagnostics research.BMC medical genomics · 2020Article
- Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.Nanomaterials (Basel, Switzerland) · 2020Review
- NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools forComputational and structural biotechnology journal · 2020Review
- Knowledge Generation with Rule Induction in Cancer Omics.International journal of molecular sciences · 2019Review
- Review
- Network-based logistic regression integration method for biomarker identification.BMC systems biology · 2018Article
- Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.BioData mining · 2017Article
- Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing.PloS one · 2017Article
- Integration of metabolomics, lipidomics and clinical data using a machine learning method.BMC bioinformatics · 2016Article
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Authors and funding
5 authors.
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Abstract
Selecting relevant features is a common task in most OMICs data analysis, where the aim is to identify a small set of key features to be used as biomarkers. To this end, two alternative but equally valid methods are mainly available, namely the univariate (filter) or the multivariate (wrapper) approach. The stability of the selected lists of features is an often neglected but very important requirement. If the same features are selected in multiple independent iterations, they more likely are reliable biomarkers. In this study, we developed and evaluated the performance of a novel method for feature selection and prioritization, aiming at generating robust and stable sets of features with high predictive power. The proposed method uses the fuzzy logic for a first unbiased feature selection and a Random Forest built from conditional inference trees to prioritize the candidate discriminant features. Analyzing several multi-class gene expression microarray data sets, we demonstrate that our technique provides equal or better classification performance and a greater stability as compared to other Random Forest-based feature selection methods.
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