Visualization and language processing for supporting analysis across the biomedical literature

Authors:
Carsten Görg;Hannah Tipney;Karin Verspoor;William A. Baumgartner, Jr.;K. Bretonnel Cohen;John Stasko;Lawrence E. Hunter
Affiliations:
School of Interactive Computing & GVU Center, Georgia Institute of Technology, Atlanta, GA;Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO;Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO;Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO;Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO;School of Interactive Computing & GVU Center, Georgia Institute of Technology, Atlanta, GA;Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO
Venue:
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part IV
Year:
2010

Citing 5
Cited 1

Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions

Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Constructing Biological Knowledge Bases by Extracting Information from Text Sources

Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Jigsaw: supporting investigative analysis through interactive visualization

Information Visualization
The Word Tree, an Interactive Visual Concordance

IEEE Transactions on Visualization and Computer Graphics
Overview of BioNLP'09 shared task on event extraction

BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task

Semantic text mining for lignocellulose research

Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Finding relevant publications in the large and rapidly growing body of biomedical literature is challenging. Search queries on PubMed often return thousands of publications and it can be a tedious task to filter out irrelevant publications and choose a manageable set to read. We have developed a visual analytics system, named Bio-Jigsaw, which acts like a visual index on a document collection and supports biologists in investigating and understanding connections between biological entities. We apply natural language processing techniques to identify biological entities such as genes and pathways and visualize connections among them via multiple representations. Connections are based on co-occurrence in abstracts and also are drawn from ontologies or annotations in digital libraries. We demonstrate how Bio-Jigsaw can be used to analyze a PubMed search query on a gene related to breast cancer resulting in over 1500 primary papers.