Description: This repository contains an extension to the δ-Trimax [1] and TriGen [2] triclustering algorithms to be able to jointly incorporate statistical significance, discriminative power criteria, and quality thresholds. We also provide a folder containing the results extracted by the modified versions of δ-Trimax and TriGen from three case studies [3,4,5].
[1] - Anirban Bhar, Martin Haubrock, Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyopadhyay, and Edgar Wingender. Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell. Algorithms for molecular biology, 8:1–11, 2013.
[2] - David Guti ́errez-Avil ́es, Cristina Rubio-Escudero, Francisco Mart ́ınez- ́Alvarez, and Jos ́e C Riquelme. Trigen: A genetic algorithm to mine triclusters in temporal gene expression data. Neurocomputing, 132:42–53, 2014.
[3] - Unknown Author. Basketball dataset. UCI Machine Learning Repository, 2019. DOI: https://doi.org/10.24432/C56G77.
[4] - Billur Barshan and Kerem Altun. Daily and Sports Activities. UCI Machine Learning Repository, 2013. DOI: https://doi.org/10.24432/C5C59F.
[5] - Palumbo, Filippo and Gallicchio, Claudio and Pucci, Rita, and Micheli, Alessio. Activity Recognition system based on Multisensor data fusion (AReM). UCI Machine Learning Repository, 2016. DOI: https://doi.org/10.24432/C5SS33