Friday, 3 November 2017

CFP: IEEE CIM Special Issue on Computational Intelligence Techniques in Bioinformatics and Bioengineering (Nov 15)

Publication: August 2018

Guest Editors

Richard Allmendinger
Alliance Manchester Business School, University of Manchester, UK

Dan Ashlock
University of Guelph, Canada

Sansanee Auephanwiriyakul
Chiang Mai University, Thailand

About IEEE Computational Intelligence Magazine (IEEE CIM)

With an impact factor of 3.647 (April 2017), the IEEE Computational Intelligence Magazine (IEEE CIM) is an influential media for publishing high-quality peer-reviewed research. CIM publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE CIS). Selection of papers for the magazine is extremely competitive. Previous special issues had an acceptance rate as minimum as 6% of submitted papers.

Aims and Scope

The focus of the special issue is on emerging topics at the interface of computational intelligence, Bioinformatics and Bioengineering.

This is a rich area that includes important and diverse problems, such as protein structure prediction, systems and synthetic biology, visualization of large-scale biological data sets, epigenomics, as well as touches on a vast range of topics in biomedical engineering, bioprocessing and healthcare informatics, such as biomedical imaging/data modelling and mining, biomarker discovery, development of personalized medicine and treatment, mining of big healthcare dataset, biopharmaceutical manufacture and computer-assisted closed-loop problems.

Many of the above problems can be framed as optimization, modeling and/or learning problems that are too difficult to tackle via classical techniques. Consequently, a consensus is emerging that current state-of-the-art approaches, such as sampling-based schemes in the Rosetta suite for macromolecular modeling, or classical mathematical programming methods, have reached a saturation point and are not very effective for vast multi-modal landscapes encountered in this domain. Over the past decade, it has become clear that increases in computational power alone will not be sufficient to combat this problem, and that there is a need for the development of specialized search and learning procedures that exploit problem-specific features and are capable of reusing information gathered during the problem solving procedure.

The field around optimization and learning via computational intelligence offers a repertoire of candidate techniques for global optimization and learning, as well as a rich body of theoretical and empirical work relating to their tuning and performance in different problem domains. Large parts of this expertise are yet to make their debut in the domain of bioinformatics and bioengineering, as the knowledge exchange between the two fields has been limited. It is only very recently that this boundary has started to break down, and promising preliminary applications have underlined the potential of this research direction. Given this, a special issue is particularly timely and will help further draw attention to this emerging research area.

The computational intelligence community in bioinformatics and bioengineering is fragmented and large. The aim of the special issue is to capture some of the ongoing interdisciplinary research that draws upon joint expertise in the domains of optimization and learning via computational intelligence techniques and bioinformatics and bioengineering. The focus is to provide both breadth in the diversity of selected problems and depth in state-of-the-art techniques for selected problems.

Topics of Interest include (but are not limited to)

Evolution, phylogeny, comparative genomics
Gene expression array analysis
Metabolic pathway analysis
MicroRNA analysis
Molecular sequence alignment and analysis
Pattern recognition/data mining/optimization methods in Bioinformatics
Visualization of large biological data sets
Systems and synthetic biology
Structure prediction and folding
Modelling, simulation, and optimization of biological systems
Biological network reconstruction/robustness/evolvability
Medical imaging and pattern recognition
Biomedical data modelling/data mining/model parametrization
Parallel/high performance computing
Big data analysis and tools for biological and medical data
Biomarker discovery and development
Health data acquisition/analysis/mining
Healthcare information systems/knowledge representation/reasoning
Personalized medicine and treatment optimization
Biopharmaceutical manufacturing
Closed-loop optimization methods/platforms

Submission Process

The IEEE CIM requires all prospective authors to submit their manuscripts in electronic format, as a PDF file. The maximum length for papers is typically 20 double-spaced typed pages with 12 point font, including figures and references. Submitted manuscript must be typewritten in English in single column format. Authors of papers should specify on the first page of their submitted manuscript up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website

The special issue is expected to include around 3-4 high quality papers.

Important Dates

15th November, 2017: Submission of manuscript
15th January, 2018: Notification of review results
15th February, 2018: Submission of revised manuscript
15th March, 2018: Submission of final manuscript
August 2018: Publication


Please feel free to contact us in case you have any questions to the special issue
Richard Allmendinger:
Dan Ashlock:
Sansanee Auephanwiriyakul:

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