Ensemble classification of cancer types and biomarker identification

Hussein Hijazi; Ming Wu; Aritro Nath; Christina Chan

doi:10.1002/ddr.21032

Ensemble classification of cancer types and biomarker identification

Hussein Hijazi, Ming Wu, Aritro Nath, Christina Chan

Experimental and Clinical Pharmacology

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

11 Scopus citations

Abstract

Preclinical Research Cancer classification is an important step in biomarker identification. Developing machine learning methods that correctly predict cancer subtypes/types can help in identifying potential cancer biomarkers. In this commentary, we presented ensemble classification approach and compared its performance with single classification approaches. Additionally, the application of cancer classification in identifying biomarkers for drug design was discussed.

Original language	English (US)
Pages (from-to)	414-419
Number of pages	6
Journal	Drug Development Research
Volume	73
Issue number	7
DOIs	https://doi.org/10.1002/ddr.21032
State	Published - Nov 2012

Keywords

biomarker
cancer classification
drug design
ensemble
gene expression

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1002/ddr.21032

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title = "Ensemble classification of cancer types and biomarker identification",

abstract = "Preclinical Research Cancer classification is an important step in biomarker identification. Developing machine learning methods that correctly predict cancer subtypes/types can help in identifying potential cancer biomarkers. In this commentary, we presented ensemble classification approach and compared its performance with single classification approaches. Additionally, the application of cancer classification in identifying biomarkers for drug design was discussed.",

keywords = "biomarker, cancer classification, drug design, ensemble, gene expression",

author = "Hussein Hijazi and Ming Wu and Aritro Nath and Christina Chan",

year = "2012",

month = nov,

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language = "English (US)",

volume = "73",

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journal = "Drug Development Research",

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TY - JOUR

T1 - Ensemble classification of cancer types and biomarker identification

AU - Hijazi, Hussein

AU - Wu, Ming

AU - Nath, Aritro

AU - Chan, Christina

PY - 2012/11

Y1 - 2012/11

N2 - Preclinical Research Cancer classification is an important step in biomarker identification. Developing machine learning methods that correctly predict cancer subtypes/types can help in identifying potential cancer biomarkers. In this commentary, we presented ensemble classification approach and compared its performance with single classification approaches. Additionally, the application of cancer classification in identifying biomarkers for drug design was discussed.

AB - Preclinical Research Cancer classification is an important step in biomarker identification. Developing machine learning methods that correctly predict cancer subtypes/types can help in identifying potential cancer biomarkers. In this commentary, we presented ensemble classification approach and compared its performance with single classification approaches. Additionally, the application of cancer classification in identifying biomarkers for drug design was discussed.

KW - biomarker

KW - cancer classification

KW - drug design

KW - ensemble

KW - gene expression

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U2 - 10.1002/ddr.21032

DO - 10.1002/ddr.21032

M3 - Review article

AN - SCOPUS:84870066963

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VL - 73

SP - 414

EP - 419

JO - Drug Development Research

JF - Drug Development Research

IS - 7

ER -

Ensemble classification of cancer types and biomarker identification

Abstract

Keywords

UN SDGs

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