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Investigation of age-related genes is of great importance for multiplepurposes, for instance, improving our understanding of the mechanism of ageing,increasing life expectancy, age prediction, and other healthcare applications.In his work, starting with a set of 27,142 genes, we develop aninformation-theoretic framework for identifying genes that are associated withaging by applying unsupervised and semi-supervised learning techniques on humandermal fibroblast gene expression data. First, we use unsupervised learning andapply information-theoretic measures to identify key features for effectiverepresentation of gene expression values in the transcriptome data. Using theidentified features, we perform clustering on the data. Finally, we applysemi-supervised learning on the clusters using different distance measures toidentify novel genes that are potentially associated with aging. Performanceassessment for both unsupervised and semi-supervised methods show theeffectiveness of the framework.

An Information-Theoretic Framework for Identifying Age-Related Genes Using Human Dermal Fibroblast Transcriptome Data