title: SVS: data and knowledge integration in computational biology.
abstract: In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.
authors.affiliation: DISI, Department of Information and Computer Science, University of Genova, I-16146 via Dodecaneso 35, Genova, Italy. fgrzegorz.zycinski@unige.it
authors.forename: Grzegorz
authors.initials: G
authors.lastname: Zycinski
authors.forename: Annalisa
authors.initials: A
authors.lastname: Barla
authors.forename: Alessandro
authors.initials: A
authors.lastname: Verri
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003201:Computers; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010300:Parkinson Disease / Q000473:pathology / Q000628:therapy; D011381:Programming Languages; D012984:Software