dRFEtools: dynamic recursive feature elimination for omics

Abstract Motivation Advances in technology have generated larger omics datasets with potential applications for machine learning. In many datasets, however, cost and limited sample availability result in an excessively higher number of features as compared to observations. Moreover, biological proce...

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Published in:Bioinformatics (Oxford, England) Vol. 39; no. 8
Main Authors: Benjamin, Kynon J M, Katipalli, Tarun, Paquola, Apuã C M
Format: Journal Article
Language:English
Published: England Oxford University Press 01.08.2023
Subjects:
Algorithms
Applications Note
Machine Learning
ISSN:1367-4811, 1367-4803, 1367-4811
Online Access:Get full text
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Summary:Abstract Motivation Advances in technology have generated larger omics datasets with potential applications for machine learning. In many datasets, however, cost and limited sample availability result in an excessively higher number of features as compared to observations. Moreover, biological processes are associated with networks of core and peripheral genes, while traditional feature selection approaches capture only core genes. Results To overcome these limitations, we present dRFEtools that implements dynamic recursive feature elimination (RFE), reducing computational time with high accuracy compared to standard RFE, expanding dynamic RFE to regression algorithms, and outputting the subsets of features that hold predictive power with and without peripheral features. dRFEtools integrates with scikit-learn (the popular Python machine learning platform) and thus provides new opportunities for dynamic RFE in large-scale omics data while enhancing its interpretability. Availability and implementation dRFEtools is freely available on PyPI at https://pypi.org/project/drfetools/ or on GitHub https://github.com/LieberInstitute/dRFEtools, implemented in Python 3, and supported on Linux, Windows, and Mac OS.
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ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btad513