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Learning Differentially Expressed Gene Pairs in Microarray Data

Xiao-Lei Xia, Sinead Brophy, Shang-ming Zhou Orcid Logo

Studies in Health Technology and Informatics, Volume: 235, Pages: 191 - 195

Swansea University Author: Shang-ming Zhou Orcid Logo

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DOI (Published version): 10.3233/978-1-61499-753-5-191

Abstract

To identify differentially expressed genes (DEGs) in analysis of microarray data, a majority of existing filter methods rank gene individually. Such a paradigm could overlook the genes with trivial individual discriminant powers but significant powers of discrimination in their combinations. This pa...

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Published in: Studies in Health Technology and Informatics
ISBN: 978-1-61499-752-8 978-1-61499-753-5
Published: 2017
Online Access: http://ebooks.iospress.nl/volumearticle/46328
URI: https://cronfa.swan.ac.uk/Record/cronfa49931
first_indexed 2019-04-09T13:04:50Z
last_indexed 2019-09-24T14:15:22Z
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spelling 2019-09-24T13:46:02.3927148 v2 49931 2019-04-08 Learning Differentially Expressed Gene Pairs in Microarray Data 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 2019-04-08 MEDS To identify differentially expressed genes (DEGs) in analysis of microarray data, a majority of existing filter methods rank gene individually. Such a paradigm could overlook the genes with trivial individual discriminant powers but significant powers of discrimination in their combinations. This paper proposed an impurity metric in which the number of split intervals for each feature is considered as a parameter to be optimized for gaining maximal discrimination. The proposed method was first evaluated by applying to a synthesized noisy rectangular grid dataset, in which the significant feature pair which forms a rectangular grid pattern was successfully recognized. Furthermore, applying to the identification of DEGs on colon microarray data, the proposed method demonstrated that it could become an alternative to Fisher's test for the prescreening of genes which led to better performance of the SVM-RFE method. Book chapter Studies in Health Technology and Informatics 235 191 195 978-1-61499-752-8 978-1-61499-753-5 30 4 2017 2017-04-30 10.3233/978-1-61499-753-5-191 http://ebooks.iospress.nl/volumearticle/46328 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University 2019-09-24T13:46:02.3927148 2019-04-08T10:22:56.3244476 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Xiao-Lei Xia 1 Sinead Brophy 2 Shang-ming Zhou 0000-0002-0719-9353 3 0049931-26042019145937.pdf 49931.pdf 2019-04-26T14:59:37.2070000 Output 285870 application/pdf Version of Record true 2019-04-25T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial License (CC-BY-NC). true eng
title Learning Differentially Expressed Gene Pairs in Microarray Data
spellingShingle Learning Differentially Expressed Gene Pairs in Microarray Data
Shang-ming Zhou
title_short Learning Differentially Expressed Gene Pairs in Microarray Data
title_full Learning Differentially Expressed Gene Pairs in Microarray Data
title_fullStr Learning Differentially Expressed Gene Pairs in Microarray Data
title_full_unstemmed Learning Differentially Expressed Gene Pairs in Microarray Data
title_sort Learning Differentially Expressed Gene Pairs in Microarray Data
author_id_str_mv 118578a62021ba8ef61398da0a8750da
author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming Zhou
author Shang-ming Zhou
author2 Xiao-Lei Xia
Sinead Brophy
Shang-ming Zhou
format Book chapter
container_title Studies in Health Technology and Informatics
container_volume 235
container_start_page 191
publishDate 2017
institution Swansea University
isbn 978-1-61499-752-8
978-1-61499-753-5
doi_str_mv 10.3233/978-1-61499-753-5-191
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
url http://ebooks.iospress.nl/volumearticle/46328
document_store_str 1
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description To identify differentially expressed genes (DEGs) in analysis of microarray data, a majority of existing filter methods rank gene individually. Such a paradigm could overlook the genes with trivial individual discriminant powers but significant powers of discrimination in their combinations. This paper proposed an impurity metric in which the number of split intervals for each feature is considered as a parameter to be optimized for gaining maximal discrimination. The proposed method was first evaluated by applying to a synthesized noisy rectangular grid dataset, in which the significant feature pair which forms a rectangular grid pattern was successfully recognized. Furthermore, applying to the identification of DEGs on colon microarray data, the proposed method demonstrated that it could become an alternative to Fisher's test for the prescreening of genes which led to better performance of the SVM-RFE method.
published_date 2017-04-30T04:46:39Z
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score 11.04748

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