title: Investigating topic models' capabilities in expression microarray data classification.
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.
authors.affiliation: Dipartimento di Informatica, Università degli Studi di Verona, Ca' Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy. manuele.bicego@univr.it
authors.forename: Manuele
authors.initials: M
authors.lastname: Bicego
authors.forename: Pietro
authors.initials: P
authors.lastname: Lovato
authors.forename: Alessandro
authors.initials: A
authors.lastname: Perina
authors.forename: Marianna
authors.initials: M
authors.lastname: Fasoli
authors.forename: Massimo
authors.initials: M
authors.lastname: Delledonne
authors.forename: Mario
authors.initials: M
authors.lastname: Pezzotti
authors.forename: Annalisa
authors.initials: A
authors.lastname: Polverari
authors.forename: Vittorio
authors.initials: V
authors.lastname: Murino
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D046228:Microarray Analysis / Q000379:methods*; D015233:Models, Statistical; D012660:Semantics