Life Sciences Commons^™

Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank

Electrical and Computer Engineering Faculty Research & Creative Works

Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene …

Identification Of Factors Affecting Bovine Somatic Cell Nuclear Transfer Efficiency And Characterization Of Transcriptional Profiles Of Nuclear Transfer Embryos And Cotyledons, Kenneth Ivan Aston

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Since the production of the first sheep by somatic cell nuclear transfer a great deal of effort has been made to improve efficiency and to understand nuclear reprogramming mechanisms. Unfortunately efficiency remains low, and nuclear reprogramming mechanisms remain uncharacterized. The objectives of this research were to identify factors associated with somatic cell nuclear transfer efficiency and to analyze the transcriptome of blastocyst-stage clone and control embryos and cotyledonary tissue in an effort to elucidate mechanisms responsible for the low developmental efficiency and high post-implantation losses.

The experiments reported here identify factors including oocyte source and timing of activation following nuclear …