Chapter 14: Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature

Rajesh Chowdhary; Boris R. Jankovic; Rachel V. Stankowski; John A.C. Archer; Xiangliang Zhang; Xin Gao; Vladimir B. Bajic

doi:10.1142/9789814551014_0014

Chapter 14: Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature

Rajesh Chowdhary, Boris R. Jankovic, Rachel V. Stankowski, John A.C. Archer, Xiangliang Zhang, Xin Gao, Vladimir B. Bajic

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Elucidation 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/).

Original language	English (US)
Title of host publication	Science, Engineering, and Biology Informatics
Publisher	World Scientific Pub Co Pte Lt
Pages	393-415
Number of pages	23
ISBN (Print)	9789814551007
DOIs	https://doi.org/10.1142/9789814551014_0014
State	Published - Dec 17 2013

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title = "Chapter 14: Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature",

abstract = "Elucidation 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/).",

author = "Rajesh Chowdhary and Jankovic, {Boris R.} and Stankowski, {Rachel V.} and Archer, {John A.C.} and Xiangliang Zhang and Xin Gao and Bajic, {Vladimir B.}",

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doi = "10.1142/9789814551014_0014",

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T1 - Chapter 14: Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature

AU - Chowdhary, Rajesh

AU - Jankovic, Boris R.

AU - Stankowski, Rachel V.

AU - Archer, John A.C.

AU - Zhang, Xiangliang

AU - Gao, Xin

AU - Bajic, Vladimir B.

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2013/12/17

Y1 - 2013/12/17

N2 - Elucidation 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/).

AB - Elucidation 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/).

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SP - 393

EP - 415

BT - Science, Engineering, and Biology Informatics

PB - World Scientific Pub Co Pte Lt

ER -

Chapter 14: Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature

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