Title

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.

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

Comments

Association of Research Libraries, Grant W911NF-18-2-0260

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

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