Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/15394
Title: Learning gene network using Bayesian network framework
Authors: LIU TIEFEI
Keywords: Gene network, Bayesian network
Issue Date: 23-Jun-2006
Source: LIU TIEFEI (2006-06-23). Learning gene network using Bayesian network framework. ScholarBank@NUS Repository.
Abstract: Learning gene networks is one of the central problems in molecular biology. In recent years, with enormous microarray data becoming available, learning gene network has received increasing attention, becoming one of the hottest topics in computational biology. However, the data problem and the complexity of gene regulatory systems make learning difficult. Moreover, some important biological factors are not considered in most published works. There factors include: various time delays among gene regulatory systems, the effects of complexes and the effect of proteins as hidden variables in microarray data. In this thesis, several learning methods based on Bayesian network framework are proposed to take into account these important biological factors: 1) The Time Delayed Network Learning (TDNL) algorithm; 2) the Conditional Dependence (CD) learning algorithm. 3) the Semi-fixed Structure Expectation Maximization (SSEM) algorithm. The effectness of the proposed methods is verified by experiments on both artificial and real-life gene expression data.
URI: http://scholarbank.nus.edu.sg/handle/10635/15394
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
genenetwork.pdf515.23 kBAdobe PDF

OPEN

NoneView/Download

Page view(s)

223
checked on Dec 11, 2017

Download(s)

188
checked on Dec 11, 2017

Google ScholarTM

Check


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