An Information-Theoretic Framework for Identifying Age-Related Genes Using Human Dermal Fibroblast Transcriptome Data
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Heteroskedasticity as a Signature of Association for Age-Related Genes
Human aging is a process controlled by both genetics and environment. Many studies have been conducted to identify a subset of genes related to aging from the human genome. Biologists implicitly categorize age-related ge…
Causal InferenceHuman AgingPositive-Unlabelled Learning for Identifying New Candidate Dietary Restriction-related Genes among Ageing-related Genes
Dietary Restriction (DR) is one of the most popular anti-ageing interventions, prompting exhaustive research into genes associated with its mechanisms. Recently, Machine Learning (ML) has been explored to identify potent…
Logistic Regression Augmented Community Detection for Network Data with Application in Identifying Autism-Related Gene Pathways
When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes related to the disease from irrelevant b…
Community DetectionMining Functionally Related Genes with Semi-Supervised Learning
The study of biological processes can greatly benefit from tools that automatically predict gene functions or directly cluster genes based on shared functionality. Existing data mining methods predict protein functionali…
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness
Node representation learning in a network is an important machine learning technique for encoding relational information in a continuous vector space while preserving the inherent properties and structures of the network…
Graph EmbeddingLink PredictionNode ClassificationRepresentation Learning