Title
Biclustering Of Gene Expression Data Based On Simui Semantic Similarity Measure
Abstract
Biclustering is an unsupervised machine learning technique that simultaneously clusters genes and conditions in gene expression data. Gene Ontology (GO) is usually used in this context to validate the biological relevance of the results. However, although the integration of biological information from different sources is one of the research directions in Bioinformatics, GO is not used in biclustering as an input data. A scatter search-based algorithm that integrates GO information during the biclustering search process is presented in this paper. SimUI is a GO semantic similarity measure that defines a distance between two genes. The algorithm optimizes a fitness function that uses SimUI to integrate the biological information stored in GO. Experimental results analyze the effect of integration of the biological information through this measure. A SimUI fitness function configuration is experimentally studied in a scatter search-based biclustering algorithm.
Year
DOI
Venue
2016
10.1007/978-3-319-32034-2_57
Hybrid Artificial Intelligent Systems
Keywords
Field
DocType
Biclustering of gene expression data, Gene pairwise GO measures, Scatter search metaheuristic
Semantic similarity,Gene ontology,Computer science,Fitness function,Unsupervised learning,Artificial intelligence,Biclustering,Machine learning
Conference
Volume
ISSN
Citations 
9648
0302-9743
2
PageRank 
References 
Authors
0.35
7
4
Name
Order
Citations
PageRank
Juan A. Nepomuceno1577.10
Alicia Troncoso215320.88
Isabel A. Nepomuceno-chamorro3417.56
Jesús S. Aguilar-ruiz462559.56