Boolean Reasoning-Based Biclustering for Shifting Pattern Extraction
Marcin Michalak, Jes\'us S. Aguilar-Ruiz

TL;DR
This paper introduces a Boolean reasoning-based biclustering method to effectively identify shifting patterns in data, especially useful for analyzing fluctuating biological data like gene expression, with promising experimental results.
Contribution
It extends Boolean reasoning to detect more general shifting patterns in biclustering, including the mathematical foundation and an effective methodology.
Findings
Successfully identifies biclusters with shifting patterns in real data
Achieves low mean squared residue (MSR) scores close to zero
Demonstrates potential for biological data analysis
Abstract
Biclustering is a powerful approach to search for patterns in data, as it can be driven by a function that measures the quality of diverse types of patterns of interest. However, due to its computational complexity, the exploration of the search space is usually guided by an algorithmic strategy, sometimes introducing random factors that simplify the computational cost (e.g. greedy search or evolutionary computation). Shifting patterns are specially interesting as they account constant fluctuations in data, i.e. they capture situations in which all the values in the pattern move up or down for one dimension maintaining the range amplitude for all the dimensions. This behaviour is very common in nature, e.g. in the analysis of gene expression data, where a subset of genes might go up or down for a subset of patients or experimental conditions, identifying functionally coherent…
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Taxonomy
TopicsGene expression and cancer classification · Data Mining Algorithms and Applications · Algorithms and Data Compression
