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Bioluminescence and Near-infrared Imaging of Optic Neuritis and Brain Inflammation in the EAE Model of Multiple Sclerosis in Mice
Published on: March 1, 2017
A comparative study of cells in inflammation, EAE and MS using biomedical literature data mining
Mathew Palakal1, John Bright, Thomas Sebastian
1Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA. mpalakal@cs.iupui.edu
Journal of Biomedical Science
|November 4, 2006
Summary
BioMap is a user-centric bioinformatics tool that helps researchers navigate vast biomedical data. It identifies associations between cells in inflammatory diseases like multiple sclerosis (MS) and its animal model, EAE.
Area of Science:
- Bioinformatics
- Computational Biology
- Biomedical Informatics
Background:
- Biomedical literature and databases contain extensive research data, overwhelming users.
- Efficiently locating relevant information is challenging due to data volume.
- Bridging the gap between biomedical research and bioinformatics tools is crucial.
Purpose of the Study:
- To develop a user-centric bioinformatics tool for customized information access.
- To validate the tool using inflammatory diseases, specifically multiple sclerosis (MS) and experimental allergic encephalomyelitis (EAE).
- To identify and elucidate associations among cells and cellular components in MS and EAE.
Main Methods:
- Developed BioMap, a user-centric bioinformatics research tool.
- Utilized literature mining, data mining, and knowledge integration techniques.
- Validated BioMap by analyzing associations in biomedical literature related to inflammation, EAE, and MS.
Main Results:
- BioMap provided a customized and adaptive view of the information space.
- Demonstrated associations between cells involved in inflammation, EAE, and MS.
- Generated association graphs exhibiting scale-free network behavior (average gamma = 2.1), common in biological networks.
Conclusions:
- BioMap effectively aids researchers in navigating complex biomedical information.
- The tool successfully identified key cellular associations in inflammatory diseases.
- The findings highlight the utility of BioMap in understanding disease mechanisms and biological networks.
