Algorithms for clustering data
Algorithms for clustering data
Modern Information Retrieval
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Meta-clustering of gene expression data and literature-based information
ACM SIGKDD Explorations Newsletter
Clustering Genes Using Gene Expression and Text Literature Data
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Gene-Ontology-based clustering of gene expression data
Bioinformatics
A knowledge-driven approach to cluster validity assessment
Bioinformatics
An effective soft clustering approach to mining gene expressions from multi-source databases
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
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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.