Conference proceeding
Extracting and mining protein-protein interaction network from biomedical literature
2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp 244-251
2004
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a biomedical literature data mining system SPIE-DM (Scalable and Portable Information Extraction and Data Mining) to extract and mine the protein-protein interaction network from biomedical literature such as MedLine. SPIE-DM consists of two phases: in phase 1, we develop a scalable and portable ie method (SPIE) to extract the protein-protein interaction from the biomedical literature. These extracted protein-protein interactions form a scale-free network graph. In phase 2, we apply a novel clustering method SFCluster to mine the protein-protein interaction network. The clusters in the network graph represent some potential protein complexes, which are very important for biologist to study the protein functionality. The clustering algorithm considers the characteristics of the scale-free network graphs and is based on the local density of the vertex and its neighborhood functions that can be used to find more meaningful clusters at different density levels. The experiments of SPIE-DM on around 1600 chromatin proteins indicate that our system is very promising for extracting and mining from biomedical literature databases.
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Details
- Title
- Extracting and mining protein-protein interaction network from biomedical literature
- Creators
- Xiaohua Hu - Drexel UniversityI Yoo - Drexel UniversityI.-Y Song - Drexel UniversityM Song - Drexel UniversityJianchao Han - California State University SystemM Lechner - Drexel Universityieee
- Publication Details
- 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp 244-251
- Conference
- 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000225765700036
- Other Identifier
- 991019170483404721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications