{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:22:40Z","timestamp":1772151760050,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2017,10,25]],"date-time":"2017-10-25T00:00:00Z","timestamp":1508889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http:\/\/mixomics.org\/mixkernel\/.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx682","type":"journal-article","created":{"date-parts":[[2017,10,24]],"date-time":"2017-10-24T15:28:27Z","timestamp":1508858907000},"page":"1009-1015","source":"Crossref","is-referenced-by-count":99,"title":["Unsupervised multiple kernel learning for heterogeneous data integration"],"prefix":"10.1093","volume":"34","author":[{"given":"J\u00e9r\u00f4me","family":"Mariette","sequence":"first","affiliation":[{"name":"MIAT, Universit\u00e9 de Toulouse, INRA, Castanet-Tolosan, France"}]},{"given":"Nathalie","family":"Villa-Vialaneix","sequence":"additional","affiliation":[{"name":"MIAT, Universit\u00e9 de Toulouse, INRA, Castanet-Tolosan, France"}]}],"member":"286","published-online":{"date-parts":[[2017,10,25]]},"reference":[{"key":"2023012712471281900_btx682-B1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1090\/S0002-9947-1950-0051437-7","article-title":"Theory of reproducing kernels","volume":"68","author":"Aronszajn","year":"1950","journal-title":"Trans. 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