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Date of Award
2017
Document Type
Campus Access Master's Report
Degree Name
Master of Science in Computer Science (MS)
Administrative Home Department
Department of Computer Science
Advisor 1
Hairong Wei
Committee Member 1
Laura Brown
Committee Member 2
Robert Pastel
Abstract
Web applications of two algorithms, Top-down graphic Gaussian model (GGM) algorithm and Bottom-up GGM algorithm, for constructing multilayered hierarchical gene regulatory networks (ML-hGRNs) were implemented. Top-down GGM Algorithm is used for building a ML-hGRN mediated by a transcription factor (TF) while Bottom-up GGM Algorithm is for constructing a ML-hGRN operating above a biological pathway. In addition, a function for plotting ML-hGRN was developed. Moreover, two mathematical methods for TF reduction were implemented. One is sparse partial least square (SPLS) for identifying a pathway associated candidate regulatory genes, which can be used as inputs for the Bottom-up GGM Algorithm; the other is an algorithm for identifying TF-responsive genes from differentially expressed genes (DEGs). These TF-responsive genes can be used as inputs for the Top-down GGM Algorithm. The web applications I developed will be instrumental for biologists to identify hierarchical regulators through construction of ML-hGRNs.
Recommended Citation
Lei, Jialin, "ONLINE DATA ANALYSIS PIPELINES FOR TOP-DOWN GRAPHICAL GAUSSIAN MODEL (GGM), BOTTOM-UP GGM AND SPARSE PARTIAL LEAST SQUARE", Campus Access Master's Report, Michigan Technological University, 2017.