MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data | Zendy

David GutiérrezAvilés | Zendy; Cristina Rubio-Escudero | Zendy

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MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data

Author(s) -

David GutiérrezAvilés,

Cristina Rubio-Escudero

Publication year - 2015

Publication title -

evolutionary bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.502

H-Index - 32

ISSN - 1176-9343

DOI - 10.4137/ebo.s25822

Subject(s) - measure (data warehouse) , biclustering , similarity (geometry) , dna microarray , data mining , cluster analysis , computer science , gene , expression (computer science) , microarray analysis techniques , computational biology , similarity measure , bioinformatics , gene expression , pattern recognition (psychology) , biology , artificial intelligence , genetics , fuzzy clustering , cure data clustering algorithm , image (mathematics) , programming language

Microarray technology is highly used in biological research environments due to its ability to monitor the RNA concentration levels. The analysis of the data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior. Biclustering relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. Triclustering appears for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. These triclusters provide hidden information in the form of behavior patterns from temporal experiments with microarrays relating subsets of genes, experimental conditions, and time points. We present an evaluation measure for triclusters called Multi Slope Measure, based on the similarity among the angles of the slopes formed by each profile formed by the genes, conditions, and times of the tricluster.

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