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A statistical framework for genomic data fusion.
- Source :
-
Bioinformatics (Oxford, England) [Bioinformatics] 2004 Nov 01; Vol. 20 (16), pp. 2626-35. Date of Electronic Publication: 2004 May 06. - Publication Year :
- 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.<br />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.<br />Availability: Supplementary data at http://noble.gs.washington.edu/proj/sdp-svm
- Subjects :
- Artificial Intelligence
Databases, Genetic
Fungal Proteins chemistry
Fungal Proteins genetics
Genomics methods
Information Storage and Retrieval methods
Membrane Proteins genetics
Membrane Proteins metabolism
Models, Statistical
Pattern Recognition, Automated
Proteins analysis
Proteins chemistry
Proteins classification
Ribosomal Proteins chemistry
Ribosomal Proteins genetics
Sequence Alignment
Sequence Homology, Amino Acid
Systems Integration
Algorithms
Chromosome Mapping methods
Databases, Protein
Gene Expression Profiling methods
Models, Genetic
Proteins genetics
Sequence Analysis, Protein methods
Subjects
Details
- Language :
- English
- ISSN :
- 1367-4803
- Volume :
- 20
- Issue :
- 16
- Database :
- MEDLINE
- Journal :
- Bioinformatics (Oxford, England)
- Publication Type :
- Academic Journal
- Accession number :
- 15130933
- Full Text :
- https://doi.org/10.1093/bioinformatics/bth294