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Application of A Novel Triclustering Method in Analyzing Three Dimensional Transcriptomics Data

dc.contributor.advisorWingender, Edgar Prof. Dr.
dc.contributor.authorBhar, Anirban
dc.titleApplication of A Novel Triclustering Method in Analyzing Three Dimensional Transcriptomics Datade
dc.contributor.refereeWingender, Edgar Prof. Dr.
dc.description.abstractengDue to the advancement of microarray technology over the last decade, it is feasible to monitor the gene expression dynamics not only over a set of replicates but also either a set of time points or doses of chemical substances. In such three dimensional datasets, variations in the expression profiles can not only be observed across the time points or doses of the chemical substances but also across the replicates due to either abnormalities in the experimental protocol or the physiological variations. Thus, it is important to mine such three dimensional datasets in order to extract biologically meaningful information. In this work, I have proposed a novel triclustering algorithm δ-TRIMAX by introducing a mean squared residue (MSR) score as a coherence measure of the resultant triclusters. The application of this algorithm has been shown in the context of breast cancer progression in order to reveal potential biological processes driving breast cancer invasion. Moreover, I have proposed an improved version of δ-TRIMAX, the EMOA-δ-TRIMAX algorithm which effectively deals with the pitfalls of the former one. One artificial dataset and three real-life datasets have been used to compare the performance of the proposed algorithms with that of other existing algorithms. Besides, the improved version has been applied to one dataset monitoring expression profiles of genes during breast cancer progression for unveiling regulatory mechanisms. Furthermore, the application of the EMOA-δ-TRIMAX algorithm has been demonstrated in investigating the potential biological processes and transcriptional regulatory mechanisms involved in the adolescence of cardiomyocytes. Additionally, I have applied EMOA-δ-TRIMAX algorithm to four real-life datasets in order to provide hints on the pathways perturbed by different toxicants in different tissues. Overall, I could demonstrate that the results of the proposed algorithms for each of the real-life datasets and the artificial ones are promising and provide new insights into the context of breast cancer progression, cardiomyocytes generation and explaining inhalation
dc.contributor.coRefereeWaack, Stephan Prof. Dr.
dc.contributor.thirdRefereeMorgenstern, Burkhard Prof. Dr.
dc.contributor.thirdRefereeSchöbel, Anita Prof. Dr.
dc.contributor.thirdRefereeBeißbarth, Tim Prof. Dr.
dc.contributor.thirdRefereeHogrefe, Dieter Prof. Dr.
dc.subject.engDevelopmental Biologyde
dc.subject.engGene Regulatory Networkde
dc.affiliation.instituteFakultät für Mathematik und Informatikde
dc.subject.gokfullInformatik (PPN619939052)de

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