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1	Inferencing Gene Regulatory Networks for Drosophila Eye Development Using an Ensemble Machine Learning Approach Abdul Jawad Mohammed (18437874) 29 April 2024 (has links) <p dir="ltr">The primary purpose of this thesis is to propose and demonstrate BioGRNsemble, a modular and flexible approach for inferencing gene regulatory networks from RNA-Seq data. Integrating the GENIE3 and GRNBoost2 algorithms, this ensembles-of-ensembles method attempts to balance the outputs of both models through averaging, before providing a trimmed-down gene regulatory network consisting of transcription and target genes. Using a Drosophila Eye Dataset, we were able to successfully test this novel methodology, and our validation analysis using an online database determined over 3500 gene links correctly detected, albeit out of almost 530,000 predictions, leaving plenty of room for improvement in the future.</p> Translational and applied bioinformatics Gene mapping Animal cell and molecular biology Statistical data science Machine Learning Bioinformatics Drosophila Melanogaster Genomics Gene Regulatory Network Eye development Computer Science genes supervised classification algorithm Biology