Please use this identifier to cite or link to this item:
Title: Graph - Based Methods for Protein Function Prediction
Keywords: protein function prediction datamining graph bioinformatics
Issue Date: 3-Apr-2008
Citation: CHUA HON NIAN (2008-04-03). Graph - Based Methods for Protein Function Prediction. ScholarBank@NUS Repository.
Abstract: With the completion of the Human Genome Project, new challenges lie ahead in deciphering the complex functional and interactive processes between proteins and multi-component molecular machines that contribute to the majority of operations in cells, as well as the transcriptional regulatory mechanisms and pathways that control these cellular processes. This thesis describes a series of graph-based methods for automated protein function prediction. These include a novel hypothesis of using the indirect interaction partners of proteins for protein function prediction, a systematic preprocessing technique for protein complex discovery, as well as an efficient probabilistic framework for integrating multiple sources of heterogeneous information for protein function prediction.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Thesis_Final.pdf1.23 MBAdobe PDF



Page view(s)

checked on May 22, 2019


checked on May 22, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.