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: |
-- | |
| Past Student Reports | ||
| No Reports Available. Contact the supervisor for more information.
Check out all available reports in the CSE Thesis Report Library. NOTE: only current CSE students can login to view and select reports to download. |
||