Synopses & Reviews
"This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.
Synopsis
The Handbook of Research on Computational Methodologies in Gene Regulatory Networks focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization.
Table of Contents
What are Gene Regulatory Networks? / Alberto de la Fuente -- Introduction to GRNs / Ugo Ala, Christian Damasco -- Bayesian Networks for Modeling and Inferring Gene Regulatory Networks / Sebastian Bauer, Peter Robinson -- Inferring Gene Regulatory Networks from Genetical Genomics Data / Bing Liu, Ina Hoeschele, Alberto de la Fuente -- Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model / Svetlana Bulashevska -- A Bayes Regularized Ordinary Differential Equation Model for the Inference of Gene Regulatory Networks / Nicole Radde, Lars Kaderali -- Computational Approaches for Modeling Intrinsic Noise and Delays in Genetic Regulatory Networks / Manuel Barrio ... [et al.] -- Modeling Gene Regulatory Networks with Delayed Stochastic Dynamics / Andre S. Ribeiro, John J. Grefenstette, Stuart A. Kauffman -- Nonlinear Stochastic Differential Equations Method for Reverse Engineering of Gene Regulatory Network / Adriana Climescu-Haulica, Michelle Quirk -- Modelling Gene Regulatory Networks Using Computational Intelligence Techniques / Ramesh Ram, Madhu Chetty -- A Synthesis Method of Gene Regulatory Networks based on Gene Expression by Network Learning / Yoshihiro Mori, Yasuaki Kuroe -- Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements / Yang Dai ... [et al.] -- Problems for Structure Learning: Aggregation and Computational Complexity / Frank Wimberly ... [et al.] --