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Combining multiple views Case studies on protein and arrhythmia features.pdf

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Combining multiple views Case studies on protein and arrhythmia features.pdf

Engineering Applications of Arti?cial Intelligence 28 (2014) 174–180 Contents lists available at ScienceDirect Engineering Applications of Arti?cial Intelligence journal homepage: /locate/engappai Combining multiple views: Case studies on protein and arrhythmia features C. Okan Sakar a,n, Olcay Kursun b, Huseyin Seker c, Fikret Gurgen d, Nizamettin Aydin e, Oleg Favorov f a Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey b Department of Computer Engineering, Istanbul University, Istanbul, Turkey c Bio-Health Informatics Research Group, Department of Informatics, De Montfort University, UK d Department of Computer Engineering, Bogazici University, Istanbul, Turkey e Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey f Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC, USA article info Article history: Received 25 January 2012 Received in revised form 10 April 2013 Accepted 4 November 2013 Available online 2 December 2013 Keywords: Multi-view learning Ensemble methods Protein structure prediction Protein sub-nuclear location prediction Arrhythmia type prediction abstract Computational annotation of protein functions and structures from sequence features, or prediction of certain diseases from gene expression levels are among important applications of computational biology. Developing methods capable of such predictions are not only important in terms of their biological and medical uses but also a very challenging task of pattern recognition due to high input dimensionality and small sample size. Ensemble and multi-view learning has gained popularity due to the rapid rise of such datasets (such as the protein and arrhythmia datasets used in this paper) with large numbers of variables. However, the classical ensemble approach does not take into account conditional interdependences among the views. In this paper, we present a two stage supervised multi-view learning technique ca