A Knowledge-Driven Method to Evaluate Multi-source Clustering

Recent research demonstrated that biological literature can complement the information extracted from gene expression data to obtain better gene clusters. The Multi-Source Clustering (MSC) algorithm, which was recently proposed by the authors, performs semantic integration of information obtained from gene expression data and biomedical text literature. To address the challenge of evaluating clustering results, a new knowledge-driven approach is proposed based on information extracted from a database of published binding sites of known transcription factors (TF). We propose the use of a measure called C-index for an objective, quantitative evaluation. We compare the results of algorithm MSC for the integrated data sources with the results obtained (a) & (b) by clustering applied to the two sources of data separately, and (c) by clustering after using a feature-level integration. We show that the C-index measurements of the clustering results from MSC are better than that from the other three approaches.

[1]  L. Hubert,et al.  Quadratic assignment as a general data analysis strategy. , 1976 .

[2]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[3]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[4]  Suzanna Becker,et al.  Mutual information maximization: models of cortical self-organization. , 1996, Network.

[5]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[6]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[7]  G. Sherlock.  Analysis of large-scale gene expression data. , 2000, Current opinion in immunology.

[8]  Hagit Shatkay,et al.  Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis , 2000, ISMB.

[9]  Xin Chen,et al.  TRANSFAC: an integrated system for gene expression regulation , 2000, Nucleic Acids Res..

[10]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[11]  M. Ashburner,et al.  The Gene Ontology Consortium , 2000 .

[12]  Javed Mostafa,et al.  Detecting Gene Relations from MEDLINE Abstracts , 2000, Pacific Symposium on Biocomputing.

[13]  R. Altman,et al.  Whole-genome expression analysis: challenges beyond clustering. , 2001, Current opinion in structural biology.

[14]  Francis D. Gibbons,et al.  Judging the quality of gene expression-based clustering methods using gene annotation. , 2002, Genome research.

[15]  Yaniv Ziv,et al.  Revealing modular organization in the yeast transcriptional network , 2002, Nature Genetics.

[16]  R. Sharan,et al.  Cluster analysis and its applications to gene expression data. , 2002, Ernst Schering Research Foundation workshop.

[17]  Jeffrey T. Chang,et al.  The computational analysis of scientific literature to define and recognize gene expression clusters. , 2003, Nucleic acids research.

[18]  Bart De Moor,et al.  Meta-clustering of gene expression data and literature-based information , 2003, SKDD.

[19]  Jung-Hsien Chiang,et al.  MeKE: Discovering the Functions of Gene Products from Biomedical Literature Via Sentence Alignment , 2003, Bioinform..

[20]  Daphne Koller,et al.  Genome-wide discovery of transcriptional modules from DNA sequence and gene expression , 2003, ISMB.

[21]  Roded Sharan,et al.  Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Boris Adryan,et al.  Gene-Ontology-based clustering of gene expression data , 2004, Bioinform..

[23]  Francisco Azuaje,et al.  A knowledge-driven approach to cluster validity assessment , 2005, Bioinform..

[24]  Giri Narasimhan,et al.  Clustering genes using gene expression and text literature data , 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).