Linked Life Data

19764809

Source:http://linkedlifedata.com/resource/pubmed/id/19764809

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2009-11-6
pubmed:abstractText
Protein complexes, integrating multiple gene products, perform all sorts of fundamental biological functions in cells. Much effort has been put into identifying protein complexes using computational approaches. A vast majority attempt to research densely connected regions in protein-protein interaction (PPI) network/graph. In this research, we try an alterative approach to analyze protein complexes using hybrid features and present a method to determine whether multiple (more than two) proteins from yeast can form a protein complex. The data set consists of 493 positive protein complexes and 9878 negative protein complexes. Every complex is represented by graph features, where proteins in the complex form a graph (web) of interactions, and features derived from biological properties including protein length, biochemical properties and physicochemical properties. These features are filtered and optimized by Minimum Redundancy Maximum Relevance method, Incremental Feature Selection and Forward Feature Selection, established through a prediction/identification model called Nearest Neighbor Algorithm. Jackknife cross-validation test is employed to evaluate the identification accuracy. As a result, the highest accuracy for the identification of the real protein complexes using filtered features is 69.17%, and feature analysis shows that, among the adopted features, graph features play the main roles in the determination of protein complexes.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1535-3907
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5212-8
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Identifying protein complexes using hybrid properties.
pubmed:affiliation
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, People's Republic of China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't