Chapter 14: Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature
Jankovic, Boris R.
Stankowski, Rachel V.
Archer, John A.C.
Bajic, Vladimir B.
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Applied Mathematics and Computational Science Program
Online Publication Date2013-12-17
Print Publication Date2014-01
Permanent link to this recordhttp://hdl.handle.net/10754/630850
MetadataShow full item record
AbstractElucidation of protein interaction networks (PINs) is essential for understanding disease-related biological processes and mechanisms. While significant information regarding PINs is available in the literature as free-text, such information is difficult to retrieve and synthesize. The following chapter describes machine learning techniques and web-based tools for literature-based extraction of protein interactions and their networks. Two case studies are provided to illustrate the efficient use of automated text-mining based applications in biomedical research using the tools PIMiner (http://www.biotextminer.com/PPI/index.html) and CPNM (http://www.biotextminer.com/CPNM/).
CitationChowdhary, R., Jankovic, B. R., Stankowski, R. V., Archer, J. A. C., Zhang, X., Gao, X., & Bajic, V. B. (2013). Chapter 14: Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature. Biological Data Mining and Its Applications in Healthcare, 393–415. doi:10.1142/9789814551014_0014
PublisherWorld Scientific Pub Co Pte Lt
JournalScience, Engineering, and Biology Informatics
Biological Data Mining and Its Applications in Healthcare