Ieee Signal Processing Magazine
نویسندگان
چکیده
iology is evolving into an informationrich science due to advances in highthroughput (large scale) experimental techniques that produce hundreds of thousands of data points in a single measurement. The field of computational functional genomics exploits this wealth of information to create models that begin to describe function in a systematic way. A fundamental aspect of computational functional genomics is the problem of functional identification. All genes in an organism have a specific function or functions. This function describes the role that a gene plays in the cell and is generally determined experimentally. The high-throughput experimental techniques mentioned above do not measure function directly, but rather measure features of a gene that are related to its function (e.g., the sequence of the gene). The relationships between the function of a gene and its features are often complex and not well understood, making machine learning algorithms ideally suited for the analysis of this type of biological data. Given a training set of genes with known features and function, a model can be constructed using machine learning to predict the function of all the genes in the organism. Furthermore, the features of an organism can be analyzed in an unsupervised framework to identify genes that have common features and thus possible common or related functions. In this article we present some specific examples of how representing biological data in a machine-learning framework is possible and how these representations contribute to both the prediction and discovery of biological function.
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