TopoBARTMAP: Biclustering ARTMAP with or Without Topological Methods in a Blood Cancer Case Study
Abstract
Biclustering is a special case of subspace clustering that has become viable in several domains. Particularly, in genomic data analysis, biclustering has been used to identify conditions under which a subset of genes are highly co-expressed, while topological data analysis has been used to analyze disease-specific subgroups, evolution, and disease progression. In this work, we combine biclustering with topological data analysis to achieve the best of both methods. We present TopoBARTMAP - produced by hybridizing BARTMAP, an adaptive resonance theory (ART)-based biclustering method, with TopoART, a topology learning ART network - in order to identify topological associations between biclusters. TopoBARTMAP outperformed both TopoART and BARTMAP in the experimental analysis on six benchmark blood cancer data sets. In some cases, BARTMAP may nevertheless be preferred due to implementation simplicity.
Recommended Citation
R. Yelugam et al., "TopoBARTMAP: Biclustering ARTMAP with or Without Topological Methods in a Blood Cancer Case Study," Proceedings of the International Joint Conference on Neural Networks (2020, Glasgow, UK), pp. 1 - 8, Institute of Electrical and Electronics Engineers (IEEE), Sep 2020.
The definitive version is available at https://doi.org/10.1109/IJCNN48605.2020.9206684
Meeting Name
2020 International Joint Conference on Neural Networks, IJCNN (2020: Jul. 19-24, Glasgow, UK)
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
International Standard Book Number (ISBN)
978-172816926-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
28 Sep 2020
Comments
Association of Research Libraries, Grant W911NF-18-2-0260