BicPAM: Pattern-based biclustering for biomedical data analysis. Biclustering, the discovery of sets of objects with a coherent pattern across a subset of conditions, is a critical task to study a wide-set of biomedical problems, where molecular units or patients are meaningfully related with a set of properties. The challenging combinatorial nature of this task led to the development of approaches with restrictions on the allowed type, number and quality of biclusters. Contrasting, recent biclustering approaches relying on pattern mining methods can exhaustively discover flexible structures of robust biclusters. However, these approaches are only prepared to discover constant biclusters and their underlying contributions remain dispersed.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Bottarelli, Lorenzo; Bicego, Manuele; Denitto, Matteo; Di Pierro, Alessandra; Farinelli, Alessandro; Mengoni, Riccardo: Biclustering with a quantum annealer (2018)
- Henriques, Rui; Madeira, Sara C.: Bsig: evaluating the statistical significance of biclustering solutions (2018)
- Veroneze, Rosana; Banerjee, Arindam; Von Zuben, Fernando J.: Enumerating all maximal biclusters in numerical datasets (2017)