  Investigation of age-related genes is of great importance for multiple
purposes, 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 an
information-theoretic framework for identifying genes that are associated with
aging by applying unsupervised and semi-supervised learning techniques on human
dermal fibroblast gene expression data. First, we use unsupervised learning and
apply information-theoretic measures to identify key features for effective
representation of gene expression values in the transcriptome data. Using the
identified features, we perform clustering on the data. Finally, we apply
semi-supervised learning on the clusters using different distance measures to
identify novel genes that are potentially associated with aging. Performance
assessment for both unsupervised and semi-supervised methods show the
effectiveness of the framework.
