Feature Selection For Multi-omics
Feature Selection for Multi-Omics is a computational methodology within multi-omics data analysis that identifies and prioritizes biologically relevant features across distinct molecular layers by integrating and reducing high-dimensional heterogeneous datasets to enhance interpretability, model performance, and biological insight.
107
Publications
11.9K
Citations
509
Authors
296
Institutions
Publications per year
2017–2026
86
107
mixOmics: An R package for ‘omics feature selection and multiple data integration
Florian Rohart, Benoît Gautier, Amrit Singh et al. · PLoS Computational Biology · 2017 · 3.9K citations · Full text
Multi-omic machine learning predictor of breast cancer therapy response
Stephen‐John Sammut, Mireia Crispin‐Ortuzar, Suet‐Feung Chin et al. · Nature · 2021 · 642 citations · Full text
Medicine, Cancer Biology, Oncology +17
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296
University of Pennsylvania
Philadelphia, United States
4 h-index
11 publications
223 citations

The University of Melbourne
Melbourne, Australia
4 h-index
7 publications
5.5K citations
Harvard University
Cambridge, United States
4 h-index
7 publications
394 citations

University of Helsinki
Helsinki, Finland
4 h-index
8 publications
416 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
4 h-index
11 publications
306 citations
University of Pittsburgh
Pittsburgh, United States
4 h-index
16 publications
544 citations
Institute for Molecular Medicine Finland
Helsinki, Finland
3 h-index
6 publications
350 citations
Dana-Farber Cancer Institute
Boston, United States
3 h-index
6 publications
244 citations
1–8 of 296
13 publications · 1.6K citations
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7 publications · 411 citations
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6 publications · 240 citations
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6 publications · 376 citations
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4 publications · 405 citations
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