Investigating Topic Models' Capabilities in Expression Microarray Data Classification

Authors:
Manuele Bicego;Pietro Lovato;Alessandro Perina;Marianna Fasoli;Massimo Delledonne;Mario Pezzotti;Annalisa Polverari;Vittorio Murino
Affiliations:
University of Verona, Verona;University of Verona, Verona;Microsoft Research, Redmond;University of Verona, Verona;University of Verona, Verona;University of Verona, Verona;University of Verona, Verona;Instituto Italiano de Tecnologia, Genova
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2012

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Abstract

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.