Abstract
The Gene Ontology (GO) knowledge base provides a standardized vocabulary of GO terms for describing gene functions and attributes. It consists of three directed acyclic graphs which represent the hierarchical structure of relationships between GO terms. GO terms enable the organization of genes based on their functional attributes by annotating genes to specific GO terms. We propose an information-retrieval derived distance between genes by using their annotations. Four gene sets with causal associations were examined by employing our proposed methodology. As a result, the discovered homogeneous subsets of these gene sets are semantically related, in contrast to comparable works. The relevance of the found clusters can be described with the help of ChatGPT by asking for their biological meaning. The R package BIDistances, readily available on CRAN, empowers researchers to effortlessly calculate the distance for any given gene set.
Subject
Applied Mathematics,Information Systems,General Medicine
Reference72 articles.
1. Unsupervised gene selection using biological knowledge: application in sample clustering;BMC Bioinformatics,2017
2. To cluster, or not to cluster: an analysis of clusterability methods;Pattern Recognition,2019
3. Biological networks;Current Opinion in Structural Biology,2003
4. Gene ontology: tool for the unification of biology;Nature Genetics,2000
5. GeneTrail—advanced gene set enrichment analysis;Nucleic Acids Research,2007