Special Issue on Bioinformatics

ACM Transactions on  Knowledge Discovery from Datahttp://www.acm.org/pubs/tkddshapeimage_1_link_0
 
 
GUEST EDITORS:
Mohammed J. Zaki, Rensselaer Polytechnic Institute, zaki@cs.rpi.edu
George Karypis, University of Minnesota, karypis@cs.umn.edu
Jiong Yang, Case Western Reserve University, jiong.yang@case.edu
Wei Wang, University of North Carolina, Chapel Hill, weiwang@cs.unc.edu
 
CALL FOR PAPERS:
Data Mining is the process of automatic discovery of novel and understandable models and patterns from large amounts of data. Bioinformatics is the science of storing, analyzing, and utilizing information from biological data such as sequences, molecules, gene expressions, and pathways. Development of novel data mining methods will play a fundamental role in understanding these rapidly expanding sources of biological data.
 
The goal of this special issue is to report on the latest research at the intersection of data mining and bioinformatics. We encourage papers that propose novel data mining techniques for tasks such as (but not necessarily restricted to):
- Gene expression analysis
- Protein/RNA structure prediction
- Phylogenetics
- Sequence and structural motifs
- Genomics and Proteomics
- Gene finding
- RNAi and microRNA Analysis
- Text mining in bioinformatics
- Modeling of biochemical pathways
 
IMPORTANT DATES:
June  15, 2007                Final deadline for submission of papers (revised)
August 17, 2007                Notification of acceptance/rejection
September 1, 2007                Deadline for having final papers at the publisher
Late 2007/Early 2008    Publication of the special issue
 
SUBMISSION INSTRUCTIONS:
Authors are encouraged to submit high quality, original work that has neither appeared in, nor is under consideration by other journals. The manuscript must follow the formatting instructions found at the TKDD web site: http://www.acm.org/pubs/tkdd. However, instead of submitting the paper via the online system, the papers for this special issue must be submitted directly as a PDF file via email to: zaki@cs.rpi.edu.