Handbook of research on computational methodologies in gene regulatory networks 1st Edition by Sanjoy Das, Doina Caragea, Stephen Welch, William Hsu, Sanjoy Das, Doina Caragea, Stephen Welch, William Hsu 1605666858 9781605666853

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ISBN 10: 1605666858 
ISBN 13: 9781605666853
Author:  Sanjoy Das, Doina Caragea, Stephen Welch, William Hsu, Sanjoy Das, Doina Caragea, Stephen Welch, William Hsu 
Recent advances in gene sequencing technology are now shedding light on the complex interplay between genes that elicit phenotypic behavior characteristic of any given organism. In order to mediate internal and external signals, the daunting task of classifying an organism’s genes into complex signaling pathways needs to be completed. 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. This innovative Handbook of Research presents a complete overview of computational intelligence approaches for learning and optimization and how they can be used in gene regulatory networks.

Handbook of research on computational methodologies in gene regulatory networks 1st Table of contents:

1. What are Gene Regulatory Networks?
2. Introduction to GRNs
3. Bayesian Networks for Modeling and Inferring Gene Regulatory Networks
4. Inferring Gene Regulatory Networks from Genetical Genomics Data
5. Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model
6. A Bayes Regularized Ordinary Differential Equation Model for the Inference of Gene Regulatory Networ
7. Computational Approaches for Modeling Intrinsic Noise and Delays in Genetic Regulatory Networks
8. Modeling Gene Regulatory Networks with Delayed Stochastic Dynamics
9. Nonlinear Stochastic Differential Equations Method for Reverse Engineering of Gene Regulatory Networ
10. Modelling Gene Regulatory Networks Using Computational Intelligence Techniques
11. A Synthesis Method of Gene Regulatory Networks based on Gene Expression by Network Learning
12. Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarra
13. Problems for Structure Learning
14. Complexity of the BN and the PBN Models of GRNs and Mappings for Complexity Reduction
15. Abstraction Methods for Analysis of Gene Regulatory Networks
16. Improved Model Checking Techniques for State Space Analysis of Gene Regulatory Networks
17. Determining the Properties of Gene Regulatory Networks from Expression Data
18. Generalized Boolean Networks
19. A Linear Programming Framework for Inferring Gene Regulatory Networks by Integrating Heterogeneous D
20. Integrating Various Data Sources for Improved Quality in Reverse Engineering of Gene Regulatory Netw
21. Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks
22. A Model for a Heterogeneous Genetic Network
23. Planning Interventions for Gene Regulatory Networks as Partially Observable Markov Decision Processe
24. Mathematical Modeling of the λ Switch
25. Petri Nets and GRN Models
26. Compilation of References

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Tags: computational, methodologies, regulatory networks, Sanjoy Das, Doina Caragea, Stephen Welch, William Hsu, Sanjoy Das, Doina Caragea, Stephen Welch, William Hsu