- From: Doina Caragea <dcaragea@ksu.edu>
- Date: Tue, 1 May 2007 09:47:15 -0500
- To: uai@ENGR.ORST.EDU, DISTRIBUTED-AI@JISCMAIL.AC.UK, SUPPORT-VECTOR-MACHINES@JISCMAIL.AC.UK, ml@isle.org, kaw@science.uva.nl, seweb-list@lists.deri.org, public-semweb-lifesci@w3.org, dl@dl.kr.org, editor@kdnuggets.com, ml@ics.uci.edu, news-announce-conferences@uunet.uu.net, dai-list@mcc.com, agents@cs.umbc.edu, community@mlnet.org, eccaiwww@eccai.org, kweb-all@lists.deri.org, ontology@listserv.ieee.org, bioinfo@dnalinux.com, bioinformatics@sdsc.edu, BIOINF-GENERAL@LISTS.UMN.EDU
- Cc: Doina Caragea <dcaragea@cis.ksu.edu>
CALL FOR CHAPTERS Submission Deadlines: proposals due on June 15, 2007, full manuscripts due on December 15, 2007 COMPUTATIONAL METHODOLOGIES IN GENE REGULATORY NETWORKS URL: www.ksu.edu/cmgrn Email: cmgrn@ksu.edu A book edited by Doina Caragea, Sanjoy Das, W. H. Hsu, Stephen M. Welch, Kansas State University, USA. (The list of authors is in alphabetical order) INTRODUCTION Recent advances in gene sequencing technology are shedding light on the complex interplay between genes that elicit phenotypic behavior characteristic of any given organism. It is now known that in order to mediate external as well as internal signals, an organism's genes are organized into complex signaling pathways. Unfortunately, unraveling the specific details about how these genetic pathways interact to regulate development, life histories, and respond to environmental cues, is proving to be a daunting task. A wide variety of models depicting gene-gene interactions, that are commonly referred to as gene regulatory networks (GRNs), have been proposed. A wide variety of computational tools are available for modeling gene regulatory networks. OVERALL OBJECTIVES A gene regulatory network (GRN) must be able to mimic experimentally observed behavior and also be computationally tractable. Under these circumstances, model simplicity is an important trade-off for functional fidelity. Modeling approaches taken by researchers are wide and disparate. Some gene regulatory networks are modeled entirely using non-parametric approaches such as Bayesian or neural networks, while some others represent genes in very physically realistic differential equation formats. The book will focus on the computational methods widely used in modeling gene regulatory networks, including structure discovery, learning and optimization. Both research and survey papers are welcome. TARGET AUDIENCE Biologists: The book can provide a comprehensive overview of computational intelligence approaches for learning and optimization and their use in gene regulatory networks to biologists. Computer Scientists: The book can assist computer scientists interested in gene regulatory network modeling. Classroom instructors and students: Although not a textbook, the book can serve as an excellent reference or supplementary material. ' Graduate students: As the book would bridge the gap between artificial intelligence and genomic research communities, it will be very useful to graduate students considering interdisciplinary research in this direction. Practicing computer scientists and geneticists: The book would be useful to those interested in gene regulatory network modeling. RECOMMENDED TOPICS Recommended topics include, but are not limited to, the following: Introduction to GRNs Introduction to graphical approaches for GRNs Bayesian network models for gene network models Petri nets and GRN models Dynamic Bayesian network GRNs Structure learning of GRNs Neural network based GRNs Boolean GRNs Temporal Boolean GRNs Probabilistic Boolean GRNs Machine learning in Boolean networks for GRNs Differential equation based GRNs Stochastic optimization algorithms for GRNs Evolutionary optimization in GRNs GRNs using the S-system formalism Optimization of S-system GRNs SUBMISSION PROCEDURE Researchers and practitioners are invited to submit on or before June 15, 2007, a 2-5 page manuscript proposal clearly explaining the mission and concerns of the proposed chapter. Authors of accepted proposals will be notified by July 15, 2007 about the status of their proposals and sent chapter organizational guidelines. Full chapters are due on December 15, 2007. All submitted chapters will be reviewed on a double-blind review basis. The book is scheduled to be published by IGI Global, www.igi-pub.com, publisher of the IGI Publishing (formerly Idea Group Publishing), Information Science Publishing, IRM Press, CyberTech Publishing and Information Science Reference (formerly Idea Group Reference) imprints. INQUIRIES Inquiries and submissions can be forwarded electronically (pdf or word document) to: cmgrn@ksu.edu More information can be found at the proposed book's website: www.ksu.edu/cmgrn Individual authors can also be contacted directly: Dr. Sanjoy Das Elect. & Comp. Engg. Dept. Kansas State University sdas@ksu.edu Tel: (785) 532-4642 Dr. Doina Caragea Comp. & Info. Sci. Dept. Kansas State University dcaragea@ksu.edu Tel: (785) 532-7908 Dr. William H. Hsu Comp. & Info. Sci. Dept. Kansas State University bhsu@ksu.edu Tel: (785) 532-6350 Dr. Stephen. M. Welch Dept. of Agronomy Kansas State University welchsm@ksu.edu Tel: (785) 532-7236
Received on Wednesday, 2 May 2007 03:18:25 UTC