Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data

2018 ◽  
pp. 55-78
Author(s):  
Anirban Mukhopadhyay
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Gene Ontology ◽  
Yeast Cell ◽  
Gene Expression Data ◽  
Data Clustering ◽  
Yeast Cell Cycle ◽  
Expression Data ◽  
Gene Expression Data Clustering ◽  
Gene Ontology Information

The Clustering Algorithm Study of Gene Expression Data

2011 ◽  
Vol 183-185 ◽  
pp. 93-98
Author(s):  
Rui He ◽  
Chun Mei Lin
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Yeast Cell ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Undirected Graph ◽  
Similarity Measurement ◽  
Expression Data ◽  
Clustering Method ◽  
Self Organized

This paper proposes an evolutionary self-organized clustering method of genes based on undirected graph expression. In this method, we use the vertices of the graph to represent genes, and regard the weight between two vertices as similarity measurement of two genes. Thus, the similarities among genes can be extracted according to the space feature of graph with immune evolutionary method. To demonstrate the effectiveness of the proposed method, the method is tested on yeast cell cycle expression dataset; the results suggest that this method is capable of clustering genes.

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