Physical Sciences and Mathematics Commons^™

Sensitivity Analysis Of Biological Boolean Networks Using Information Fusion Based On Nonadditive Set Functions, Naomi Kochi, Tomáš Helikar, Laura Allen, Jim A. Rogers, Zhenyuan Wang, Mihaela Teodora Matache

Mathematics Faculty Publications

Background: An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network.

Results: We find that for a biochemical signal transduction network consisting of several main signaling pathways whose nodes represent signaling …

A New Nonlinear Classifier With A Penalized Signed Fuzzy Measure Using Effective Genetic Algorithm, Julia Hua Fang, Maria L. Rizzo, Honggang Wang, Kimberly Espy, Zhenyuan Wang

Mathematics Faculty Publications

This paper proposes a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification accuracy and power by capturing all possible interactions among two or more attributes. This generalized approach was developed to address unsolved Choquet-integral classification issues such as allowing for flexible location of projection lines in n-dimensional space, automatic search for the least misclassification rate based on Choquet distance, and penalty on misclassified points. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Both the numerical experiment and empirical case studies show that this generalized …