Classification algorithms for phenotype prediction in genomics and proteomics

Habtom W. Ressom; Rency S. Varghese; Zhen Zhang; Jianhua Xuan; Robert Clarke

doi:10.2741/2712

Classification algorithms for phenotype prediction in genomics and proteomics

Habtom W. Ressom, Rency S. Varghese, Zhen Zhang, Jianhua Xuan, Robert Clarke

School of Medicine

Research output: Contribution to journal › Review article › peer-review

53 Scopus citations

Abstract

This paper gives an overview of statistical and machine learning-based feature selection and pattern classification algorithms and their application in molecular cancer classification or phenotype prediction. In particular, the paper focuses on the use of these computational methods for gene and peak selection from microarray and mass spectrometry data, respectively. The selected features are presented to a classifier for phenotype prediction.

Original language	English (US)
Pages (from-to)	691-708
Number of pages	18
Journal	Frontiers in Bioscience
Volume	13
Issue number	2
DOIs	https://doi.org/10.2741/2712
State	Published - 2008

Keywords

Classification
Feature selection
Gene expression
Mass spectrometry
Microarray
Review

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology
General Immunology and Microbiology

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10.2741/2712

Cite this

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title = "Classification algorithms for phenotype prediction in genomics and proteomics",

abstract = "This paper gives an overview of statistical and machine learning-based feature selection and pattern classification algorithms and their application in molecular cancer classification or phenotype prediction. In particular, the paper focuses on the use of these computational methods for gene and peak selection from microarray and mass spectrometry data, respectively. The selected features are presented to a classifier for phenotype prediction.",

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AU - Varghese, Rency S.

AU - Zhang, Zhen

AU - Xuan, Jianhua

AU - Clarke, Robert

PY - 2008

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AB - This paper gives an overview of statistical and machine learning-based feature selection and pattern classification algorithms and their application in molecular cancer classification or phenotype prediction. In particular, the paper focuses on the use of these computational methods for gene and peak selection from microarray and mass spectrometry data, respectively. The selected features are presented to a classifier for phenotype prediction.

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KW - Feature selection

KW - Gene expression

KW - Mass spectrometry

KW - Microarray

KW - Review

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Classification algorithms for phenotype prediction in genomics and proteomics

Abstract

Keywords

ASJC Scopus subject areas

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