Problems for Structure Learning: Aggregation and Computational Complexity

Problems for Structure Learning: Aggregation and Computational Complexity

Frank Wimberly (Carnegie Mellon University (retired), USA), David Danks (Carnegie Mellon University, USA), Clark Glymour (Carnegie Mellon University, USA) and Tianjiao Chu (University of Pittsburgh, USA)
Copyright: © 2010 |Pages: 24
DOI: 10.4018/978-1-60566-685-3.ch013

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 the structure of a genetic regulatory network from microarray data alone, nor do we think that such a proof is likely. However, the combination of (i) fundamental challenges from theory, (ii) practical evidence that those challenges arise in realistic data, and (iii) the difficulty of avoiding those challenges leads us to conclude that it is unlikely that current microarray technology will ever be successfully applied to this structure learning problem.
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Theory: Learning From Aggregations

Microarrays are small chips a few square inches in size on which spots of DNA have been imbedded. A typical chip may contain thousands of spots, each spot composed of multiple copies of a small sequence of DNA. In the living cell nucleus, sections of DNA are copied (“transcribed”) into a dual complementary molecule, RNA, which is the scaffolding for the synthesis, outside the cell nucleus, of cellular proteins. RNA can be extracted from tissue, and tiny luminescent beads can be chemically attached to RNA molecules obtained from tissue cells (e.g., from breast cancer cells). Each RNA molecule contains a sequence of bases that binds to a specific DNA sequence. When a suspension consisting of many RNA molecules from a tissue sample is applied to a microarray, the RNA molecules bind to the complementary DNA sites. By measuring the luminosity of each DNA spot, the relative concentration of each kind of RNA in the tissue sample can be estimated. From these concentrations, one can infer relative activity of genes—how much RNA is produced by various parts of the cell DNA in the tissues sampled.

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