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(in press). In S. Das, D. Caragea, W. H. Hsu, & S. M. Welch (Eds.), Computational methodologies in gene regulatory networks. Hershey, PA: IGI Global Publishing.
 

Summary: (in press). In S. Das, D. Caragea, W. H. Hsu, & S. M. Welch (Eds.), Computational methodologies in gene
regulatory networks. Hershey, PA: IGI Global Publishing.
Problems for Structure Learning:
Aggregation and Computational
Complexity
Frank Wimberly
Carnegie Mellon University (retired), USA
David Danks
Carnegie Mellon University and Institute for Human & Machine Cognition, USA
Clark Glymour
Carnegie Mellon University and Institute for Human & Machine Cognition, USA
Tianjiao Chu
University of Pittsburgh, USA
ABSTRACT
Machine learning methods to find graphical models of genetic regulatory networks from cDNA
microarray data have become increasingly popular in recent years. We provide three reasons to
question the reliability of such methods: (1) a major theoretical challenge to any method using
conditional independence relations; (2) a simulation study using realistic data that confirms the
importance of the theoretical challenge; and (3) an analysis of the computational complexity of
algorithms that avoid this theoretical challenge. We have no proof that one cannot possibly learn

  

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

 

Collections: Mathematics