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Inferring Adaptive Regulation Thresholds and Association Rules from Gene Expression Data

Summary: Inferring Adaptive Regulation Thresholds and
Association Rules from Gene Expression Data
through Combinatorial Optimization Learning
Ignacio Ponzoni, Francisco J. Azuaje, Juan Carlos Augusto, and David H. Glass
Abstract--There is a need to design computational methods to support the prediction of gene regulatory networks (GRNs). Such
models should offer both biologically meaningful and computationally accurate predictions which, in combination with other techniques,
may improve large-scale integrative studies. This paper presents a new machine-learning method for the prediction of putative
regulatory associations from expression data which exhibit properties never or only partially addressed by other techniques recently
published. The method was tested on a Saccharomyces cerevisiae gene expression data set. The results were statistically validated
and compared with the relationships inferred by two machine-learning approaches to GRN prediction. Furthermore, the resulting
predictions were assessed using domain knowledge. The proposed algorithm may be able to accurately predict relevant biological
associations between genes. One of the most relevant features of this new method is the prediction of adaptive regulation thresholds
for the discretization of gene expression values, which is required prior to the rule association learning process. Moreover, an important
advantage consists of its low computational cost to infer association rules. The proposed system may significantly support exploratory
large-scale studies of automated identification of potentially relevant gene expression associations.
Index Terms--Combinatorial optimization, genetic regulatory networks, machine learning, gene expression data, decision trees.

A gene regulatory network (GRN) aims to represent
high-level relationships that govern the rates at which


Source: Augusto, Juan Carlos - School of Computing and Mathematics, University of Ulster at Jordanstown


Collections: Computer Technologies and Information Sciences