Book chapter
A Knowledge-Driven Method to Evaluate Multi-source Clustering
Parallel and Distributed Processing and Applications - ISPA 2005 Workshops, pp.196-202
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2005
DOI: 10.1007/11576259_22
Abstract
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.
Details
- Title: Subtitle
- A Knowledge-Driven Method to Evaluate Multi-source Clustering
- Creators
- Chengyong Yang - Bioinformatics Research Group (BioRG), School of Computer Science, Florida International University, Miami, USAErliang Zeng - Bioinformatics Research Group (BioRG), School of Computer Science, Florida International University, Miami, USATao Li - Bioinformatics Research Group (BioRG), School of Computer Science, Florida International University, Miami, USAGiri Narasimhan - Bioinformatics Research Group (BioRG), School of Computer Science, Florida International University, Miami, USA
- Resource Type
- Book chapter
- Publication Details
- Parallel and Distributed Processing and Applications - ISPA 2005 Workshops, pp.196-202
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/11576259_22
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2005
- Academic Unit
- Preventive and Community Dentistry; Biostatistics; Roy J. Carver Department of Biomedical Engineering; Dental Research; Iowa Neuroscience Institute
- Record Identifier
- 9984070855302771
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