Bioinformatics Advance Access originally published online on May 6, 2004
Bioinformatics 2004 20(16):2626-2635; doi:10.1093/bioinformatics/bth294
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Bioinformatics vol. 20 issue 16 © Oxford University Press 2004; all rights reserved.
A statistical framework for genomic data fusion
1 Department of Electrical Engineering and Computer Science, 2 Division of Computer Science, Department of Statistics, University of California, Berkeley 94720, USA, 3 Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven 3001, Belgium, 4 Department of Statistics, University of California, Davis 95618, USA and 5 Department of Genome Sciences, University of Washington, Seattle 98195, USA
Received on January 29, 2004; revised on April 7, 2004; accepted on April 23, 2004
Advance Access Publication May 6, 2004
Motivation: During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data.
Results: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein–protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins—membrane proteins and ribosomal proteins—performs significantly better than the same algorithm trained on any single type of data.
Availability: Supplementary data at http://noble.gs.washington.edu/proj/sdp-svm
Contact: noble{at}gs.washington.edu
* To whom correspondence should be addressed at: Health Sciences Center, Box 357730, 1705 NE Pacific Street, Seattle, WA 98195, USA.
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