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From pattern to causality: using linear discriminant analysis and Bayesian network on microarray data of breast cancers

Monash University
Chien-Yu Chen (Aggregated by) Ru-Sheng Liu (Aggregated by) Shu-Yuan Chen (Aggregated by) Tsun-Chen Lin (Aggregated by) Ya-Ting Chao (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:doi10.4225/03/5a13729325a4e&rft.title=From pattern to causality: using linear discriminant analysis and Bayesian network on microarray data of breast cancers&rft.identifier=https://doi.org/10.4225/03/5a13729325a4e&rft.publisher=Monash University&rft.description=In this paper, we aim at using genetic algorithms for gene selection and propose silhouette statistics as a discriminant function to classify breast cancers on microarray data for pattern discovery. In order to see the causality among these genes, we use the Bayesian method to construct a probability network for the pattern discovered. Consequently, we found a set of genes that is effective to discriminate breast cancer subtypes and present their probability dependencies to construct a diagnostic system. 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=Chien-Yu Chen&rft.creator=Ru-Sheng Liu&rft.creator=Shu-Yuan Chen&rft.creator=Tsun-Chen Lin&rft.creator=Ya-Ting Chao&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=Genetic algorithm&rft_subject=Silhouette statistics&rft_subject=Bayesian network&rft_subject=Microarray&rft_subject=Classification&rft_subject=Breast cancer&rft_subject=2008&rft_subject=conference paper&rft_subject=1959.1/63714&rft_subject=monash:7864&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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In this paper, we aim at using genetic algorithms for gene selection and propose silhouette statistics as a discriminant function to classify breast cancers on microarray data for pattern discovery. In order to see the causality among these genes, we use the Bayesian method to construct a probability network for the pattern discovered. Consequently, we found a set of genes that is effective to discriminate breast cancer subtypes and present their probability dependencies to construct a diagnostic system. 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.

Issued: 2017-11-21

Created: 2017-11-21

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1959.1/63714 | 2008 | Bayesian network | Bioinformatics | Bioinformatics -- Congresses | Bioinformatics Software | Breast cancer | Classification | Computational biology -- Congresses | Computational biology -- Methods -- Congresses | Computer vision in medicine -- Congresses | Genetic algorithm | Microarray | Pattern Recognition and Data Mining | Pattern recognition, automated -- Methods -- Congresses | Silhouette statistics | conference paper | monash:7864 |

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