Paper Info
Title
Employing publically available biological expert knowledge from protein-protein interaction information
Abstract
Genome wide association studies (GWAS) are now allowing researchers to probe the depths of common complex human diseases, yet few have identified single sequence variants that confer disease susceptibility. As hypothesized, this is due the fact that multiple interacting factors influence clinical endpoint. Given the number of single nucleotide polymorphisms (SNPs) combinations grows exponentially with the number of SNPs being analyzed, computational methods designed to detect these interactions in smaller datasets are thus not applicable. Providing statistical expert knowledge has exhibited an improvement in their performance, and we believe biological expert knowledge to be as capable. Since one of the strongest demonstrations of the functional relationship between genes is protein-protein interactions, we present a method that exploits this information in genetic analyses. This study provides a step towards utilizing expert knowledge derived from public biological sources to assist computational intelligence algorithms in the search for epistasis.
Year
DOI
Venue
2010
10.1007/978-3-642-16001-1_34
PRIB
Keywords
Field
DocType
biological expert knowledge,protein-protein interaction information,publically available biological expert,single nucleotide polymorphism,public biological source,single sequence variant,computational method,common complex human disease,clinical endpoint,computational intelligence algorithm,utilizing expert knowledge,statistical expert knowledge,genome wide association study,snps,genetics,protein protein interaction,epistasis,gwas,computational intelligence
Protein–protein interaction,Computational intelligence,Epistasis,Computer science,Genome-wide association study,Artificial intelligence,Single-nucleotide polymorphism,Bioinformatics,Machine learning
Conference
Volume
ISSN
ISBN
6282
0302-9743
3-642-16000-X
Citations 
PageRank 
References 
0
0.34
10
Authors
3
Name
Order
Citations
PageRank
Kristine A. Pattin101.01
Jiang Gui212.46
Jason H. Moore31223159.43