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Gene expression data analysis using novel methods: Predicting time delayed correlations and evolutionarily conserved functional modules

Microarray technology enables the study of gene expression on a large scale. One of the main challenges has been to devise methods to cluster genes that share similar expression profiles. In gene expression time courses, a particular gene may encode transcription factor and thus controlling several...

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Main Author: Balasubramaniyan, Rajarajeswari
Contributors: Kämper Jörg (Dr. ) (Thesis advisor)
Format: Doctoral Thesis
Language:English
Published: Philipps-Universität Marburg 2005
Subjects:
Funktionelle Module
Optimierung
Datenanalyse
Genexpr
Molekulare Bioinformatik
Globale Optimierung
Optimierungsproblem
Biowissenschaften, Biologie
Time-delayed correlation
Saccharomyces cerevisiae
Zeitversetzte Korrelationen
Ustilago zeae
Functional Modules
Life sciences
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Call Number: urn:nbn:de:hebis:04-z2005-01342
Publication Date: 2005-09-09
Source: Balasubramaniyan, R., Hüllermeier, E., Weskamp, N., Kämper, J. (2005). Clustering of Gene Expression Data Using a Local Shape-Based Similarity Measure, Bioinformatics 21, 1069-1077.
Date of Acceptance: 2005-07-22
Downloads: 32 (2025), 119 (2024), 49 (2023), 115 (2022), 71 (2021), 44 (2020), 36 (2019), 14 (2018)
License: https://rightsstatements.org/vocab/InC-NC/1.0/
Access URL: https://archiv.ub.uni-marburg.de/diss/z2005/0134
https://doi.org/10.17192/z2005.0134

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