Algorithms for clustering data
Algorithms for clustering data
Information retrieval
An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Information Retrieval Meets Gene Analysis
IEEE Intelligent Systems
Mining for Putative Regulatory Elements in the Yeast Genome Using Gene Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Analysis of Gene Expression Data with Pathway Scores
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Clustering Genes Using Gene Expression and Text Literature Data
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Combining full text and bibliometric information in mapping scientific disciplines
Information Processing and Management: an International Journal - Special issue: Infometrics
Artificial Intelligence in Medicine
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
Multiple view semi-supervised dimensionality reduction
Pattern Recognition
Combining full text and bibliometric information in mapping scientific disciplines
Information Processing and Management: an International Journal - Special issue: Infometrics
A knowledge-driven method to evaluate multi-source clustering
ISPA'05 Proceedings of the 2005 international conference on Parallel and Distributed Processing and Applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The current tendency in the life sciences to spawn ever growing amounts of high-throughput assays has led to a situation where the interpretation of data and the formulation of hypotheses lag the pace at which information is produced. Although the first generation of statistical algorithms scrutinizing single, large-scale data sets found their way into the biological community, the great challenge to connect their results to existing knowledge still remains. Despite the fairly large number of biological databases that is currently available, a lot of relevant information is found in free-text format (such as textual annotations, scientific abstracts and full publications). In this paper we explore how an integrated analysis of expression data and literature-extracted information can reveal biologically meaningful clusters not identified when using microarray information alone. The joint analysis is validated in terms of transcriptional regulation.