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Investigating Topic Models' Capabilities in Expression Microarray Data Classification
Nov.-Dec. 2012 (vol. 9 no. 6)
pp. 1831-1836
M. Bicego, Dipt. di Inf., Univ. degli Studi di Verona, Verona, Italy
P. Lovato, Dipt. di Inf., Univ. degli Studi di Verona, Verona, Italy
A. Perina, Microsoft Res., Redmond, WA, USA
M. Fasoli, Dipt. di Biotecnologie, Univ. degli Studi di Verona, Verona, Italy
M. Delledonne, Dipt. di Biotecnologie, Univ. degli Studi di Verona, Verona, Italy
M. Pezzotti, Dipt. di Biotecnologie, Univ. degli Studi di Verona, Verona, Italy
A. Polverari, Dipt. di Biotecnologie, Univ. degli Studi di Verona, Verona, Italy
V. Murino, Anal. & Comput. Vision (PAVIS), Ist. Italiano di Tecnol. (IIT), Genoa, Italy
In recent years a particular class of probabilistic graphical models-called topic models-has proven to represent an useful and interpretable tool for understanding and mining microarray data. In this context, such models have been almost only applied in the clustering scenario, whereas the classification task has been disregarded by researchers. In this paper, we thoroughly investigate the use of topic models for classification of microarray data, starting from ideas proposed in other fields (e.g., computer vision). A classification scheme is proposed, based on highly interpretable features extracted from topic models, resulting in a hybrid generative-discriminative approach; an extensive experimental evaluation, involving 10 different literature benchmarks, confirms the suitability of the topic models for classifying expression microarray data.
Index Terms:
probability,biology computing,computer vision,data mining,feature extraction,genetic algorithms,genetics,graphs,molecular biophysics,pattern classification,computer vision,topic model capability,microarray data classification expression,probabilistic graphical models,microarray data mining,clustering scenario,classification task,highly interpretable features extraction,hybrid generative-discriminative approach,extensive experimental evaluation,literature benchmarks,molecular biology,genetics,Biological system modeling,Data models,Computational modeling,Probabilistic logic,Feature extraction,Analytical models,hybrid generative discriminative approaches,Expression microarray,topic models
Citation:
M. Bicego, P. Lovato, A. Perina, M. Fasoli, M. Delledonne, M. Pezzotti, A. Polverari, V. Murino, "Investigating Topic Models' Capabilities in Expression Microarray Data Classification," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 6, pp. 1831-1836, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.121
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