Akman, Olcay

Hrozencik, Daniel

English

ISU ReD: Research and eData

On The Implementation of Particle SwarmOptimization for Optimizing Artificial NeuralNetworks In Gene Regulatory Network PropensityEstimationOlcay Akman1,*, Dan Hrozencik21Department of Mathematics, Illinois State University, Normal, IL 617902Department of Mathematics & Computer Science, Chicago State University, Chicago, IL 60628oakman@ilstu.eduThis paper presents an innovative method for optimizing the state transition variability of gene regulatorynetworks (GRNs) due stochastic variation of propensity probabilities caused by internal noise at the molec-ular level. Using neural network modeling optimized via Particle Swarm Optimization the state transitionvariability was measured to be within reasonable bounds. Results show impressive agreement with theoret-ical predictions and significant improvement over previous efforts by Akman et al. The work presented herehas profound implications for future studies of stochastic GRN modeling and may one day help solve theproblem of cancer origination.

On The Implementation of Particle Swarm Optimization for Optimizing Artificial Neural Networks in Gene Regulatory Network Propensity Estimation

https://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1332&amp;context=beer

On The Implementation of Particle Swarm Optimization for Optimizing Artificial Neural Networks in Gene Regulatory Network Propensity Estimation

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ISU ReD: Research and eData