Identifying interactions in omics data for clinical biomarker discovery using symbolic regression | Zendy

N. J. Christensen | Zendy; Samuel Demharter | Zendy; Meera Machado | Zendy; Lykke Pedersen | Zendy; Marco Salvatore | Zendy; Valdemar Stentoft-Hansen | Zendy; Miquel Triana | Zendy

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Identifying interactions in omics data for clinical biomarker discovery using symbolic regression

Author(s) -

N. J. Christensen,

Samuel Demharter,

Meera Machado,

Lykke Pedersen,

Marco Salvatore,

Valdemar Stentoft-Hansen,

Miquel Triana

Publication year - 2022

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac405

Subject(s) - interpretability , python (programming language) , computer science , machine learning , omics , toolbox , identification (biology) , documentation , data mining , biomarker discovery , artificial intelligence , bioinformatics , biology , proteomics , programming language , biochemistry , botany , gene

The identification of predictive biomarker signatures from omics and multi-omics data for clinical applications is an active area of research. Recent developments in assay technologies and machine learning (ML) methods have led to significant improvements in predictive performance. However, most high-performing ML methods suffer from complex architectures and lack interpretability.

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