Clustering genes using gene expression and text literature data

Chengyong Yang; Erliang Zeng; Tao Li; Giri Narasimhan

doi:10.1109/CSB.2005.23

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Conference proceeding

Clustering genes using gene expression and text literature data

Chengyong Yang, Erliang Zeng, Tao Li and Giri Narasimhan

2005 IEEE Computational Systems Bioinformatics Conference (CSB'05), Vol.2005, pp.329-340

2005

DOI: 10.1109/CSB.2005.23

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Abstract

Clustering of gene expression data is a standard technique used to identify closely related genes. In this paper, we develop a new clustering algorithm, MSC (Multi-Source Clustering), to perform exploratory analysis using two or more diverse sources of data. In particular, we investigate the problem of improving the clustering by integrating information obtained from gene expression data with knowledge extracted from biomedical text literature. In each iteration of algorithm MSC, an EM-type procedure is employed to bootstrap the model obtained from one data source by starting with the cluster assignments obtained in the previous iteration using the other data sources. Upon convergence, the two individual models are used to construct the final cluster assignment. We compare the results of algorithm MSC for two data sources with the results obtained when the clustering is applied on the two sources of data separately. We also compare it with that obtained using the feature level integration method that performs the clustering after simply concatenating the features obtained from the two data sources. We show that the z-scores of the clustering results from MSC are better than that from the other methods. To evaluate our clusters better, function enrichment results are presented using terms from the Gene Ontology database. Finally, by investigating the success of motif detection programs that use the clusters, we show that our approach integrating gene expression data and text data reveals clusters that are biologically more meaningful than those identified using gene expression data alone.

Bioinformatics

Computer Science

Data Analysis

Data Mining

Gene Expression

Algorithm design and analysis

Biological Literature

Text Mining

Information analysis

Databases

Clustering algorithms

Gene Expression Data

Performance analysis

Multi-Source Clustering

Details

Title: Subtitle: Clustering genes using gene expression and text literature data
Creators: Chengyong Yang - Bioinformatics Res. Group, Florida Int. Univ., Miami, FL, USA
Erliang Zeng - Bioinformatics Res. Group, Florida Int. Univ., Miami, FL, USA
Tao Li - Bioinformatics Res. Group, Florida Int. Univ., Miami, FL, USA
Giri Narasimhan - Bioinformatics Res. Group, Florida Int. Univ., Miami, FL, USA
Resource Type: Conference proceeding
Publication Details: 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05), Vol.2005, pp.329-340
DOI: 10.1109/CSB.2005.23
Publisher: IEEE
Language: English
Date published: 2005
Academic Unit: Preventive and Community Dentistry; Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute; Biostatistics; Dental Research
Record Identifier: 9984065472202771

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