{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T08:57:55Z","timestamp":1768726675734,"version":"3.49.0"},"reference-count":34,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2017,7,8]],"date-time":"2017-07-08T00:00:00Z","timestamp":1499472000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes are still diagnosed based on individual data sources (e.g. gene expression data). We propose an unsupervised data integration method based on kernel principal component analysis. Principal component analysis is one of the most widely used techniques in data analysis. Unfortunately, the straightforward multiple kernel extension of this method leads to the use of only one of the input matrices, which does not fit the goal of gaining information from all data sources. Therefore, we present a scoring function to determine the impact of each input matrix. The approach enables visualizing the integrated data and subsequent clustering for cancer subtype identification. Due to the nature of the method, no hyperparameters have to be set. We apply the methodology to five different cancer data sets and demonstrate its advantages in terms of results and usability.<\/jats:p>","DOI":"10.1515\/jib-2017-0019","type":"journal-article","created":{"date-parts":[[2017,7,8]],"date-time":"2017-07-08T10:01:27Z","timestamp":1499508087000},"source":"Crossref","is-referenced-by-count":5,"title":["Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples"],"prefix":"10.1515","volume":"14","author":[{"given":"Nora K.","family":"Speicher","sequence":"first","affiliation":[{"name":"Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbr\u00fccken, Germany"}]},{"given":"Nico","family":"Pfeifer","sequence":"additional","affiliation":[{"name":"Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbr\u00fccken, Germany"},{"name":"Methods in Medical Informatics, Department of Computer Science, University of T\u00fcbingen, T\u00fcbingen, Germany"}]}],"member":"374","reference":[{"key":"ref271","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TPAMI.2007.250598","article-title":"Graph embedding and extensions: A general framework for dimensionality reduction","volume":"29","year":"2007","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref01","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1126\/science.276.5316.1268","article-title":"Gene expression profiles in normal and cancer cells","volume":"276","year":"1997","journal-title":"Science"},{"key":"ref261","first-page":"327","volume-title":"Advances in kernel methods","year":"1999"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nmeth.2810","article-title":"Similarity network fusion for aggregating data types on a genomic scale","volume":"11","year":"2014","journal-title":"Nat Methods"},{"key":"ref141","volume-title":"Kernel methods for pattern analysis","year":"2004"},{"key":"ref91","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TPAMI.2010.183","article-title":"Multiple kernel learning for dimensionality reduction","volume":"33","year":"2011","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref251","doi-asserted-by":"crossref","first-page":"i268","DOI":"10.1093\/bioinformatics\/btv244","article-title":"Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery","volume":"31","year":"2015","journal-title":"Bioinformatics"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.1073\/pnas.1208949110","article-title":"Pattern discovery and cancer gene identification in integrated cancer genomic data","volume":"110","year":"2013","journal-title":"Proc Natl Acad Sci"},{"key":"ref161","first-page":"2","article-title":"Component retention in principal component analysis with application to cDNA microarray data. 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