Thesis Topic Details

Topic ID:
305
Title:
Mining Biomedical Abstracts
Supervisor:
Mike Bain
Research Area:
Associated Staff
Assessor:
Mark Temple
Topic Details
Status:
Active
Type:
Research
Programs:
CS CE BIOM BINF SE
Group Suitable:
No
Industrial:
Pre-requisites:
See proposer.
Description:
Online text documents are becoming increasingly important to many organisations. This is the case in the area of bioinformatics, where a wide range of journals publish abstracts on the Web where they are freely accessible. In most cases, however, it is impossible for researchers to keep abreast of the amount of material which may contain something of interest to them. The particular task we will focus on is using text categorization and information extraction on biomedical abstracts to generate predictive classifiers for experimental data.
Comments:
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Past Student Reports
  Bryan Krishen ROGERS in s2, 2013
Mining Biomedical Abstracts
 

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