Topological biclustering ARTMAP for identifying within bicluster relationships

Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphic...

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Bibliographic Details
Published in:Neural networks 2023-03, Vol.160, p.34-49
Main Authors: Yelugam, Raghu, Brito da Silva, Leonardo Enzo, Wunsch II, Donald C.
Format: Article
Language:English
Subjects:
Adaptive resonance theory (ART)
Algorithms
Biclustering
Cluster Analysis
Gene Co-expression
Gene expression
Gene Expression Profiling - methods
Topological data analysis
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Summary:Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphical representation. Therefore, to improve the quality of biclustering and module extraction, this work combines the adaptive resonance theory (ART)-based methods of biclustering ARTMAP (BARTMAP) and topological ART (TopoART), to produce TopoBARTMAP. The latter inherits the ability to detect topological associations while performing data reduction. The capabilities of TopoBARTMAP were benchmarked using 35 real world cancer datasets and contrasted with other (bi)clustering methods, where it showed a statistically significant improvement over the other assessed methods on ordered and shuffled data experiments. In experiments with 12 synthetic datasets, the method was observed to perform better at identifying constant, scale, shift, and shift scale type biclusters. The produced graphical representation was refined to represent gene bicluster associations and was assessed on the NCBI GSE89116 dataset containing expression levels of 39,326 probes sampled over 38 observations.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2022.12.010