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A Subset Of Arabidopsis Ap2 Transcription Factors Mediates Cytokinin Responses In Concert With A Two-Component Pathway, Aaron M. Rashotte, Michael G. Mason, Claire E. Hutchison, Fernando J. Ferreira, G. Eric Schaller, Joseph J. Kieber
A Subset Of Arabidopsis Ap2 Transcription Factors Mediates Cytokinin Responses In Concert With A Two-Component Pathway, Aaron M. Rashotte, Michael G. Mason, Claire E. Hutchison, Fernando J. Ferreira, G. Eric Schaller, Joseph J. Kieber
Dartmouth Scholarship
The plant hormone cytokinin regulates numerous growth and developmental processes. A signal transduction pathway for cytokinin has been elucidated that is similar to bacterial two-component phosphorelays. In Arabidopsis, this pathway is comprised of receptors that are similar to sensor histidine kinases, histidine-containing phosphotransfer proteins, and response regulators (ARRs). There are two classes of response regulators, the type-A ARRs, which act as negative regulators of cytokinin responses, and the type-B ARRs, which are transcription factors that play a positive role in mediating cytokinin-regulated gene expression. Here we show that several closely related members of the Arabidopsis AP2 gene family of …
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Dartmouth Scholarship
The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.