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2016 | OriginalPaper | Buchkapitel

TRIQ: A Comprehensive Evaluation Measure for Triclustering Algorithms

verfasst von : David Gutiérrez-Avilés, Cristina Rubio-Escudero

Erschienen in: Hybrid Artificial Intelligent Systems

Verlag: Springer International Publishing

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Abstract

Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. Triclustering relaxes the constraints for grouping and allows genes to be evaluated under a subset of experimental conditions and a subset of time points simultaneously. The authors previously presented a genetic algorithm, TriGen, that finds triclusters of gene expression dasta. They also defined three different fitness functions for TriGen: \(MSR_{3D}\), LSL and MSL. In order to asses the results obtained by application of TriGen, a validity measure needs to be defined. Therefore, we present TRIQ, a validity measure which combines information from three different sources: (1) correlation among genes, conditions and times, (2) graphic validation of the patterns extracted and (3) functional annotations for the genes extracted.

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Vorheriges Kapitel Motivational Engine with Sub-goal Identification in Neuroevolution Based Cognitive Robotics

Nächstes Kapitel Biclustering of Gene Expression Data Based on SimUI Semantic Similarity Measure

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Titel: TRIQ: A Comprehensive Evaluation Measure for Triclustering Algorithms
verfasst von: David Gutiérrez-Avilés
Cristina Rubio-Escudero
Verlag: Springer International Publishing
Buch: Hybrid Artificial Intelligent Systems
Print ISBN: 978-3-319-32033-5

Electronic ISBN: 978-3-319-32034-2

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-32034-2_56

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