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.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Bayesian networks for modeling
Boolean networks
Computational approaches for modeling
Computational Intelligence Techniques
Gene regulatory networks
Genetical genomics data
Heterogeneous genetic networks
Markov decision process
Microarray gene expression measurements
Reverse Engineering

Reviews & Statements

This book provides a bird's eye view of the vast range of computational methods used to model GRNs. It contains introductory material and surveys, as well as articles describing in-depth research in various aspects of GRN modeling.

– Sanjoy Das, Kansas State University, USA

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Editor/Author Biographies

Sanjoy Das is an associate professor in the Department of Electrical & Computer Engineering at Kansas State University. He received a Ph.D. in Electrical Engineering from Louisiana State University in 1994. He was a postdoctoral researcher at the University of California, Berkeley and the Smith-Kettlewell Institute between 1994 and 1997. Until 2001 he held various research appointments in the industry. Prof. Das’s research interests include computational intelligence, bio-inspired computing, and their applications to genomics (especially gene regulatory network modeling). He has published over 100 research papers in journals, books and conference proceedings. His research has been funded by the U.S. National Science Foundation, the U.S. Department of Agriculture, and the U.S. Department of Defense.

Doina Caragea is an assistant professor at Kansas State University. Her research interests include artificial intelligence, machine learning, data mining, information integration and information visualization, with applications to bioinformatics. Doina received her Ph.D. in Computer Science from Iowa State University in August 2004 and was honored with the Iowa State University Research Excellence Award for her achievements. Her Ph.D. work at Iowa State University was focused on learning classifiers from autonomous, distributed, semantically heterogeneous data sources. Her recent work at Kansas State University has been focused on the development of algorithms and tools for genome annotation. More specifically, she has participated in projects such as EST data analysis, investigation of transcription networks and their relation to environment, and studies on alternative splicing, among others. Prof. Caragea has published more than 30 refereed conference and journal articles. She is teaching machine learning, data mining and bioinformatics courses.

Stephen Welch is a professor at Kansas State University. His focus is gene networks, plant phenology, optimal parameter estimation, and parallel computing, with applications in ecological genomics and plant breeding. He has a B.S. in Computer Science (1971) and a Ph.D. in Zoology (1977), both from Michigan State University. The common thread in his career has been computer simulation of living systems in both the departments of entomology and (since 1990) agronomy. Short term activities have included service as Acting State Climatologist for Kansas and Interim Director of University Computing and Network Services. Recent work has involved modeling the genetic control of Arabidopsis flowering time as part of a multinational collaboration with field sites from Spain to Finland. Under the auspices of the iPlant Collaborative funded by the US National Science Foundation, he also co-leads an international team developing a cyberinfrastructure for grand challenge research that interrelates plant genotypes and phenotypes. He has 61 peer reviewed papers, conference proceedings, and book chapters, plus 78 publications of other types.

William H. Hsu is an associate professor of Computing and Information Sciences at Kansas State University. He received a B.S. in Mathematical Sciences and Computer Science and an M.S.Eng. in Computer Science from Johns Hopkins University in 1993, and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1998. His dissertation explored the optimization of inductive bias in supervised machine learning for predictive analytics. At the National Center for Supercomputing Applications (NCSA) he was a co-recipient of an Industrial Grand Challenge Award for visual analytics of text corpora. His research interests include machine learning, probabilistic reasoning, and information visualization, with applications to cybersecurity, education, digital humanities, geoinformatics, and biomedical informatics. Published applications of his research include structured information extraction; spatiotemporal event detection for veterinary epidemiology, crime mapping, and opinion mining; analysis of heterogeneous information networks. Current work in his lab deals with: data mining and visualization in education research; graphical models of probability and utility for information security; developing domain-adaptive models of large natural language corpora and social media for text mining, link mining, sentiment analysis, and recommender systems. Dr. Hsu has over 50 refereed publications in conferences, journals, and books, plus over 35 additional publications.

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