Society for Mathematical Biology

Bioinformatics applies - simply stated - computational methods to the solution of biological problems. Bioinformatics, genomics, molecular biology, molecular evolution, computational biology, and affine fields are at the intersection between two axes: data sequences/physiology and information technology. Sequences include DNA sequences (gene, genome, organization), molecular evolution, protein structure, folding, function, and interaction, metabolic pathways, regulation signaling networks, physiology and cell biology (interspecies, interaction), as well as ecology and environment. Information technology in this context includes hardware and instrumentation, computation, as well as mathematical and physical models. The intersection between two subfields, one in each axis, generates areas including those known as genome sequencing, proteomics, functional genomics (microarrays, 2D-PAGE, etc.), high-tech field ecology, genomic data analysis, statistical genomics, protein structure, prediction, protein dynamics, protein folding and design, data standards, data representations, analytical tools for complex biological data, dynamical systems modeling, as well as computational ecology. Research in these fields comprises property abstraction from the biological system, design and development of data analysis algorithms, as well as of databases and data access web-tools. Genome sequencing and related projects generate vast amounts of data that needs to be analyzed, thus emphasizing the relevance of efficient methods of data analysis and of the whole discipline.

The Neurocomputing journal invites original contributions for the forthcoming special issue on Bioinformatics from a broad scope of areas. Some topics relevant to this special issue include, but are not restricted to:

Theoretical foundations, algorithms, implementations, and complete systems
Sequence analysis (single, multiple), alignment, annotation, etc.
Improvements in databases and web-tools for bioinformatics
Novel metrics and biological data preprocessing for posterior analysis
Systems biology models and data modeling techniques including statistical inference, stochastic processes, random walks, Markov chains, hidden Markov models, motifs, profiles, dynamic programming, pattern recognition techniques, neural networks, support vector machines, evolutionary models, tree estimation, etc.
Pathway inference, e.g. to determine where to target a drug using gene expression data and address side effects by providing information on where else a target metabolite appears.
Key applications in diverse fields including bioinformatics, genomics, molecular biology, molecular evolution, computational biology, drug design, etc.

Please send two hardcopies of the manuscript before July 31st, 2002, to:

V. David Sanchez, Neurocomputing - Editor in Chief -
Advanced Computational Intelligent Systems
P.O. Box 60130,
Pasadena, CA 91116-6130, U.S.A.

Street address:

1149 Wotkyns Drive
Pasadena, CA 91103, U.S.A.
Fax: +1-626-793-5120
Email: vdavidsanchez@earthlink.net
Website: http://www.elsevier.nl/locate/neucom

including abstract, keywords, a cover page containing the title and author names, corresponding author name's complete address including telephone, fax, and email address, and clear indication to be a submission to the Special Issue on Bioinformatics.

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