Journal Guide

Algorithms (ISSN 1999-4893; CODEN: ALGOCH) is an open access journal of computer science, theory, methods and interdisciplinary applications, data and information systems, software engineering, artificial intelligence, automation and control systems. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, unique features of this journal:, , manuscripts regarding research proposals and research ideas will be particularly welcomed, electronic files or software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material, we also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds, copies of source codes might be deposited.

Algorithms for Molecular Biology is ready to receive manuscripts on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning.

BioData Mining publishes original articles on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data.

The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.

The Bulletin of Mathematical Biology is devoted to research at the junction of computational, theoretical and experimental biology. Articles offer a combination of theory and experiment, documenting theoretical advances with clear exposition of how they further biological understanding. Its aim is to be of major interest to theorists and experimental biologists alike.The Bulletin of mathematical biology also invites submissions of review articles which offer an in-depth treatment of an emerging research area, or a significant recent development, in the general area of computational, theoretical and mathematical biology. Those interested in writing such a review article should contact James Sneyd at sneyd@math.auckland.ac.nz This is the official journal of the Society for Mathematical Biology.

COMPEL provides a platform for innovations and developments of techniques and methodology employed in computation in electrical and electronic engineering.

Complex Analysis and Operator Theory (CAOT) is devoted to the publication of current research developments in the closely related fields of complex analysis and operator theory as well as in applications to system theory, harmonic analysis, probability, statistics, learning theory, and other related fields. Articles using the theory of reproducing kernel spaces are in particular welcomed. CAOT is published in four regular and four sectional issues per year, the latter organised in two sections of two issues each. One section concentrates on Higher Dimensional Geometric Function Theory and Hypercomplex Analysis; the other focusses on Infinite-dimensional Analysis and Non-commutative Theory.   Bibliographic Data
Complex Anal. Oper. Theory
First published in 2007
1 volume per year, 6 issues per volume
approx. 1200 pages per volume
Format: 15.5 x 23.5 cm
ISSN 1661-8254 (print)
ISSN 1661-8262 (electronic)

AMS Mathematical Citation Quotient (MCQ): 0.56 (2011)

computational complexity presents outstanding research in computational complexity. Its subject is at the interface between mathematics and theoretical computer science, with a clear mathematical profile and strictly mathematical format. The central topics are: Models of computation, complexity bounds (with particular emphasis on lower bounds), complexity classes, trade-off results

for sequential and parallel computationfor 'general' (Boolean) and 'structured' computation (e.g. decision trees, arithmetic circuits)for deterministic, probabilistic, and nondeterministic computationworst case and average caseSpecific areas of concentration include:

Structure of complexity classes (reductions, relativization questions, degrees, derandomization)Algebraic complexity (bilinear complexity, computations for polynomials, groups, algebras, and representations)Cryptography, interactive proofs, pseudorandom generation

Complexity issues in:
learning theorynumber theorylogic (complexity of logical theories

Computational Geometry is a forum for research in theoretical and applied aspects of computational geometry. The journal publishes fundamental research in all areas of the subject, as well as disseminating information on the applications, techniques, and use of computational geometry. Computational Geometry publishes articles on the design and analysis of geometric algorithms. All aspects of computational geometry are covered, including the numerical, graph theoretical and combinatorial aspects. Also welcomed are computational geometry solutions to fundamental problems arising in computer graphics, pattern recognition, robotics, image processing, CAD-CAM, VLSI design and geographical information systems.Computational Geometry features a special section containing open problems and concise reports on implementations of computational geometry tools.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

Computational Geosciences publishes high quality papers on mathematical modeling, simulation, numerical analysis, and other computational aspects of the geosciences. In particular the journal is focused on advanced numerical methods for the simulation of subsurface flow and transport, and associated aspects such as discretization, gridding, upscaling, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing. Papers treating similar topics but with applications to other fields in the geosciences, such as geomechanics, geophysics, oceanography, or meteorology, will also be considered. The journal provides a platform for interaction and multidisciplinary collaboration among diverse scientific groups, from both academia and industry, which share an interest in developing mathematical models and efficient algorithms for solving them, such as mathematicians, engineers, chemists, physicists, and geoscientists.

Computational Mechanics reports original research in computational mechanics of enduring scholarly value. It focuses on areas that involve and enrich the rational application of mechanics, mathematics, and numerical methods in the practice of modern engineering. The journal investigates theoretical and computational methods and their rational applications. Areas covered include solid and structural mechanics, multi-body system dynamics, constitutive modeling, inelastic and finite deformation response, and structural control. The journal also covers fluid mechanics and fluid-structure interactions, biomechanics, free-surface and two-fluid flows, aerodynamics, fracture mechanics and structural integrity, multi-scale mechanics, particle and meshfree methods, transport phenomena, and heat transfer. Lastly, the journal publishes modern variational methods in mechanics in general.

CMFT is an international mathematics journal which publishes carefully selected original research papers in complex analysis (in a broad sense), and on applications or computational methods related to complex analysis. Survey articles of high standard and current interest can be considered for publication as well. Contributed papers should be written in English (exceptions in rare cases are tolerated), and in a lucid, expository style. Papers should not exceed 30 printed pages.

Computational Statistics & Data Analysis (CSDA), the official journal of the International Association of Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of three refereed sections, and a fourth section dedicated to news on statistical computing. The refereed sections are divided into the following subject areas:I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics, computational econometrics, computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article.II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, econometrics, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures.Statistical methodology includes, but not limited to: bootstrapping, classification techniques, clinical trials, data exploration, density estimation, design of experiments, pattern recognition/image analysis, parametric and nonparametric methods, statistical genetics, Bayesian modeling, outlier detection, robust procedures, cross-validation, functional data, fuzzy statistical analysis, mixture models, model selection and assessment, nonlinear models, partial least squares, latent variable models, structural equation models, supervised learning, signal extraction and filtering, time-series modelling, longitudinal analysis, multilevel analysis and quality control.III) Special Applications - Manuscripts at the interface of statistics and computing (e.g., comparison of statistical methodologies, computer-assisted instruction for statistics, simulation experiments). Advanced statistical analysis with real applications (economics, social sciences, marketing, psychometrics, chemometrics, signal processing, finance, medical statistics, environmentrics, statistical physics).