Protein function prediction using semantic driven K-medoids clustering algorithm
Journal
International Journal of Machine Learning and Computing
Date Issued
2014-02-01
Author(s)
Abstract
The proposed protein function prediction methods
are mostly based on sequence or structure protein similarity
and do not take into account the semantic similarity extracted
from protein knowledge databases such as Gene Ontology.
Many studies have shown that identification of protein
complexes or functional modules can be effectively done by
clustering protein interaction network (PIN). A significant
number of proteins in such PIN remain uncharacterized and
predicting their function remains a major challenge in system
biology. In this paper we present a “semantic driven” clustering
approach for protein function prediction by using both
semantic similarity metrics and the whole network topology of a
PIN. We apply k-medoids clustering combined with several
semantic similarity metrics as a weight factor in the
distance-clustering matrix. Protein functions are assigned based
on cluster information. Results reveal improvement over
standard non-semantic similarity metric.
are mostly based on sequence or structure protein similarity
and do not take into account the semantic similarity extracted
from protein knowledge databases such as Gene Ontology.
Many studies have shown that identification of protein
complexes or functional modules can be effectively done by
clustering protein interaction network (PIN). A significant
number of proteins in such PIN remain uncharacterized and
predicting their function remains a major challenge in system
biology. In this paper we present a “semantic driven” clustering
approach for protein function prediction by using both
semantic similarity metrics and the whole network topology of a
PIN. We apply k-medoids clustering combined with several
semantic similarity metrics as a weight factor in the
distance-clustering matrix. Protein functions are assigned based
on cluster information. Results reveal improvement over
standard non-semantic similarity metric.
Subjects
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