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

TL;DR

This paper introduces an information-theoretic framework combining unsupervised and semi-supervised learning to identify age-related genes from human dermal fibroblast transcriptome data, advancing aging research.

Contribution

It presents a novel framework that integrates information-theoretic measures with clustering and semi-supervised learning for gene aging analysis.

Findings

Effective identification of key gene features.

Successful clustering of gene expression data.

Discovery of novel age-associated genes.

Abstract

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.