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.
Recommended Citation
S. Petrenko et al., "Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory," Information (Switzerland), vol. 15, no. 3, article no. 125, MDPI, Mar 2024.
The definitive version is available at https://doi.org/10.3390/info15030125
Department(s)
Chemistry
Second Department
Electrical and Computer Engineering
Third Department
Computer Science
Publication Status
Open Access
Keywords and Phrases
adaptive resonance theory; biomedical data; categorical data; knowledge graphs; ontologies
International Standard Serial Number (ISSN)
2078-2489
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Mar 2024
Included in
Chemistry Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
