Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory
Abstract
Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multi-categorical symbolic data. This article proposes symbolic tree adaptive resonance theory (START), with additional supervised, dual-vigilance (DV-START), and distributed dual-vigilance (DDV-START) formulations, for the clustering of multi-categorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot–Marie–Tooth disease using genomic, phenotypic, and proteomic data.
Department(s)
Cooperative Engineering Program
Document Type
Article
DOI
10.3390/info15030125
Keywords
adaptive resonance theory, biomedical data, categorical data, knowledge graphs, ontologies
Publication Date
3-1-2024
Recommended Citation
Obafemi-Ajayi, Tayo; Petrenko, Sasha; Hier, Daniel B.; Bone, Mary A.; Timpson, Erik J.; Marsh, William E.; Speight, Michael; and Wunsch, Donald C., "Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory" (2024). Faculty Scholarship. 405.
https://bearworks.missouristate.edu/articles00/405
Journal Title
Information Switzerland
