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A statistical framework for genomic data fusion



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BIOINFORMATICS Vol 20 no 16 2004 pages 2626 2635 doi 10 1093 bioinformatics bth294 A statistical framework for genomic data fusion Gert R G Lanckriet1 Tijl De Bie3 Nello Cristianini4 Michael I Jordan2 and William Stafford Noble5 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 ABSTRACT 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 genomewide datasets including amino acid sequences hydropathy profiles gene expression data and known protein protein interactions demonstrate the utility of this



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