Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a bette...

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Bibliographic Details
Published in:Genes Vol. 12; no. 11; p. 1814
Main Authors: Han, Yuanyuan, Huang, Lan, Zhou, Fengfeng
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 18.11.2021
MDPI
Subjects:
Algorithms
Alzheimer's disease
Behavior
Biomarkers
Biomarkers - metabolism
Classification
Data analysis
data collection
Datasets
Epigenome
Feature selection
Gene expression
Genomes
Genomics - methods
Genomics - standards
Humans
Intelligence
Learning algorithms
Lung cancer
Machine Learning
Optimization algorithms
prediction
Prediction models
Software - standards
swarms
Technical Note
Transcriptome
Transcriptomes
Transcriptomics
zoos
prediction
machine learning
program code
feature selection
swarm intelligence
ISSN:2073-4425, 2073-4425
Online Access:Get full text
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Summary:Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.
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ISSN:2073-4425
2073-4425
DOI:10.3390/genes12111814