One of the central problems of functional genomics is revealing gene expression networks – the relationships between genes that reflect observations of how the expression level of each gene affects those of others. Microarray data are currently a major source of information about the interplay of biochemical network participants in living cells. Various mathematical techniques, such as differential equations, Bayesian and Boolean models and several statistical methods, have been applied to expression data in attempts to extract the underlying knowledge. Unsupervised clustering methods are often considered as the necessary first step in visualization and analysis of the expression data. As for supervised classification, the problem mainly addressed so far has been how to find discriminative genes separating various samples or experimental conditions. Numerous methods have been applied to identify genes that help to predict treatment outcome or to confirm a diagnosis, as well as to identify primary elements of gene regulatory circuits. However, less attention has been devoted to using supervised learning to uncover relationships between genes and/or their products. To start filling this gap a machine-learning approach for gene networks reconstruction is described here. This approach is based on building classifiers – functions, which determine the state of a gene's transcription machinery through expression levels of other genes. The method can be applied to various cases where relationships between gene expression levels could be expected.

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