caa a22 4500 445871350 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20110901xx s 000 0 eng 10.1007/s10618-010-0205-7 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0205-7 Matching samples of multiple views [Elektronische Daten] [Abhishek Tripathi, Arto Klami, Matej Orešič, Samuel Kaski] Multi-view learning studies how several views, different feature representations, of the same objects could be best utilized in learning. In other words, multi-view learning is analysis of co-occurrence data, where the observations are co-occurrences of samples in the views. Standard multi-view learning such as joint density modeling cannot be done in the absence of co-occurrence, when the views are observed separately and the identities of objects are not known. As a practical example, joint analysis of mRNA and protein concentrations requires mapping between genes and proteins. We introduce a data-driven approach for learning the correspondence of the observations in the different views, in order to enable joint analysis also in the absence of known co-occurrence. The method finds a matching that maximizes statistical dependency between the views, which is particularly suitable for multi-view methods such as canonical correlation analysis which has the same objective. We apply the method to translational metabolomics, to identify differences and commonalities in metabolic processes in different species or tissues. The metabolite identities and roles in the different species are not generally known, and it is necessary to search for a matching. In this paper we show, using different metabolomics measurement batches as the views so that the ground truth is known, that the metabolite identities can be reliably matched by a consensus of several matching solutions. The Author(s), 2010 Bipartite matching nationallicence Canonical correlation nationallicence Consensus matching nationallicence Co-occurrence data nationallicence Multi-view learning nationallicence Tripathi Abhishek Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland aut Klami Arto Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Helsinki, Finland aut Orešič Matej Quantitative Biology and Bioinformatics, VTT Technical Research Centre of Finland, Espoo, Finland aut Kaski Samuel Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, Helsinki, Finland aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/2(2011-09-01), 300-321 1384-5810 23:2<300 2011 23 10618 https://doi.org/10.1007/s10618-010-0205-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0205-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tripathi Abhishek Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland aut NATIONALLICENCE 700 1- Klami Arto Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Helsinki, Finland aut NATIONALLICENCE 700 1- Orešič Matej Quantitative Biology and Bioinformatics, VTT Technical Research Centre of Finland, Espoo, Finland aut NATIONALLICENCE 700 1- Kaski Samuel Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, Helsinki, Finland aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/2(2011-09-01), 300-321 1384-5810 23:2<300 2011 23 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer