Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data
 

Summary: Constructing Bayesian Network Models of Gene
Expression Networks from Microarray Data
Peter Spirtes a , Clark Glymour b , Richard Scheines a , Stuart Kauffman c , Valerio Aimale c , Frank Wimberly c
a Department of Philosophy, Carnegie Mellon University
b Institute for Human and Machine Cognition c Bios Group
1. Introduction
Through their transcript products genes regulate
the rates at which an immense variety of transcripts and
subsequent proteins occur. Understanding the
mechanisms that determine which genes are expressed,
and when they are expressed, is one of the keys to
genetic manipulation for many purposes, including the
development of new treatments for disease.
Viewing each gene in a genome as a distinct
variable that is either on (expresses) or off (does not
express), or more realistically as a continuous variable
(the rate of expression), the values of some of these
variables influence the values of others through the
regulatory proteins they express, including, of course,
the possibility that the rate of expression of a gene at

  

Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University

 

Collections: Mathematics