article

Gene selection using locality sensitive laplacian score

Editor: Ying Xu Authors:

Zhi CaoAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 11, Issue 6

Pages 1146 - 1156

https://doi.org/10.1109/TCBB.2014.2328334

Published: 01 November 2014 Publication History

Abstract

Gene selection based on microarray data, is highly important for classifying tumors accurately. Existing gene selection schemes are mainly based on ranking statistics. From manifold learning standpoint, local geometrical structure is more essential to characterize features compared with global information. In this study, we propose a supervised gene selection method called locality sensitive Laplacian score (LSLS), which incorporates discriminative information into local geometrical structure, by minimizing local within-class information and maximizing local between-class information simultaneously. In addition, variance information is considered in our algorithm framework. Eventually, to find more superior gene subsets, which is significant for biomarker discovery, a two-stage feature selection method that combines the LSLS and wrapper method (sequential forward selection or sequential backward selection) is presented. Experimental results of six publicly available gene expression profile data sets demonstrate the effectiveness of the proposed approach compared with a number of state-of-the-art gene selection methods.

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Cited By

Wang JSaxena APatel D(2024)A Machine Learning-Based Wrapper Method for Feature SelectionInternational Journal of Data Warehousing and Mining10.4018/IJDWM.35204120:1(1-33)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.4018/IJDWM.352041
Pattanateepapon ASuwansantisuk WKumhom P(2021)Methods to transform microarray data for cancer prediction2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB.2016.7758104(1-7)Online publication date: 10-Mar-2021
https://dl.acm.org/doi/10.1109/CIBCB.2016.7758104
Wang PGe RXiao XZhou MZhou F(2017)hMuLabIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2016.260350714:5(1173-1180)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1109/TCBB.2016.2603507
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Index Terms

Gene selection using locality sensitive laplacian score
1. Applied computing
  1. Life and medical sciences

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Information

Published In

cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 11, Issue 6

November/December 2014

290 pages

ISSN:1545-5963

Editor:
Ying Xu
Department of Biochemistry, Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA

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Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 November 2014

Accepted: 22 May 2014

Revised: 13 April 2014

Received: 25 January 2014

Published in TCBB Volume 11, Issue 6

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Cited By

Wang JSaxena APatel D(2024)A Machine Learning-Based Wrapper Method for Feature SelectionInternational Journal of Data Warehousing and Mining10.4018/IJDWM.35204120:1(1-33)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.4018/IJDWM.352041
Pattanateepapon ASuwansantisuk WKumhom P(2021)Methods to transform microarray data for cancer prediction2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB.2016.7758104(1-7)Online publication date: 10-Mar-2021
https://dl.acm.org/doi/10.1109/CIBCB.2016.7758104
Wang PGe RXiao XZhou MZhou F(2017)hMuLabIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2016.260350714:5(1173-1180)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1109/TCBB.2016.2603507
Maji PShah E(2017)Significance and Functional Similarity for Identification of Disease GenesIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2016.259816314:6(1419-1433)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1109/TCBB.2016.2598163
Ang JMirzal AHaron HHamed H(2016)Supervised, Unsupervised, and Semi-Supervised Feature SelectionIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2015.247845413:5(971-989)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1109/TCBB.2015.2478454
Liao BJiang YLiang WPeng LPeng LHanyurwimfura DLi ZChen M(2015)On Efficient Feature Ranking Methods for High-Throughput Data AnalysisIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2015.241579012:6(1374-1384)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1109/TCBB.2015.2415790
Tabakhi SNajafi ARanjbar RMoradi P(2015)Gene selection for microarray data classification using a novel ant colony optimizationNeurocomputing10.1016/j.neucom.2015.05.022168:C(1024-1036)Online publication date: 30-Nov-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.05.022

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