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Feature ranking and feature redundancy reduction for prognostic microarray study of tumor clinical outcomes

Monash University
Kaare Christensen (Aggregated by) Mads Thomassen (Aggregated by) Qihua Tan (Aggregated by) Torben A. Kruse (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/03/5a1372383442b&rft.title=Feature ranking and feature redundancy reduction for prognostic microarray study of tumor clinical outcomes&rft.identifier=https://doi.org/10.4225/03/5a1372383442b&rft.publisher=Monash University&rft.description=Different from significant gene expression analysis which looks for all genes that are differentially regulated, feature selection in prognostic gene expression analysis aims at finding a subset of informative marker genes that are discriminative for prediction. Unfortunately feature selection in the literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significance. Since the univariate approach does not take into account the correlated or interactive structure among the genes, classifiers built on genes so selected can be less accurate. More advanced approaches based on multivariate models have to be considered. Here, we introduce a feature ranking method through forward orthogonal search to assist prognostic gene selection. Application to published gene-lists selected by univariate models shows that the feature space can be largely reduced while achieving improved testing performances. Our results indicate that significant features selected using the gene-wised approaches can contain irrelevant genes that only serve to complicate model building. Multivariate feature ranking can help to reduce feature redundancy and to select highly informative prognostic marker genes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.&rft.creator=Kaare Christensen&rft.creator=Mads Thomassen&rft.creator=Qihua Tan&rft.creator=Torben A. Kruse&rft.date=2017&rft_rights=In Copyright&rft_subject=Bioinformatics -- Congresses&rft_subject=Computational biology -- Congresses&rft_subject=Computer vision in medicine -- Congresses&rft_subject=Computational biology -- Methods -- Congresses&rft_subject=Pattern recognition, automated -- Methods -- Congresses&rft_subject=Feature selection&rft_subject=Clinical outcome prediction&rft_subject=Microarray data&rft_subject=2008&rft_subject=conference paper&rft_subject=1959.1/63701&rft_subject=monash:7858&rft_subject=Bioinformatics&rft_subject=Pattern Recognition and Data Mining&rft_subject=Bioinformatics Software&rft.type=dataset&rft.language=English Access the data
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Different from significant gene expression analysis which looks for all genes that are differentially regulated, feature selection in prognostic gene expression analysis aims at finding a subset of informative marker genes that are discriminative for prediction. Unfortunately feature selection in the literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significance. Since the univariate approach does not take into account the correlated or interactive structure among the genes, classifiers built on genes so selected can be less accurate. More advanced approaches based on multivariate models have to be considered. Here, we introduce a feature ranking method through forward orthogonal search to assist prognostic gene selection. Application to published gene-lists selected by univariate models shows that the feature space can be largely reduced while achieving improved testing performances. Our results indicate that "significant" features selected using the gene-wised approaches can contain irrelevant genes that only serve to complicate model building. Multivariate feature ranking can help to reduce feature redundancy and to select highly informative prognostic marker genes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ;
Chetty, Madhu ;
Ahmad, Shandar ;
Ngom, Alioune ;
Teng, Shyh Wei ;
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ;
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Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

Issued: 2017-11-21

Created: 2017-11-21

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1959.1/63701 | 2008 | Bioinformatics | Bioinformatics -- Congresses | Bioinformatics Software | Clinical outcome prediction | Computational biology -- Congresses | Computational biology -- Methods -- Congresses | Computer vision in medicine -- Congresses | Feature selection | Microarray data | Pattern Recognition and Data Mining | Pattern recognition, automated -- Methods -- Congresses | conference paper | monash:7858 |

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