Paper submission deadline: 15th November 2017
Publication: August 2018
Alliance Manchester Business School, University of Manchester, UK
University of Guelph, Canada
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 theIEEE 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
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.
Please submit your paper via https://easychair.org/conferences/?conf=ieeecimcitbb2018.
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: firstname.lastname@example.org
Dan Ashlock: email@example.com
Sansanee Auephanwiriyakul: firstname.lastname@example.org
About the guest editors
Dr Richard Allmendinger is a Lecturer at the Alliance Manchester Business School (Alliance MBS), The University of Manchester, UK. Richard has joined Alliance MBS in 2015 after a 4-year long stint as Postdoc at the EPSRC Centre for Innovative Manufacturing in Emergent Macromolecular Therapies, University College London (UCL), UK. He received a PhD degree in Computer Science from the University of Manchester, UK, and a Diplom in Industrial Engineering from the Karlsruhe Institute of technology (KIT), Germany.
Dr Daniel Ashlock is a Professor in the Department of Mathematics and Statistics at the University of Guelph in Canada. Dr. Ashlock's work in bioinformatics and optimization includes the invention of side effect machines, woven string kernals, and other forms of optimization that support DNA classification. Dr. Ashlock is also a leading researcher in the creation of sequencing-error tolerant embeddable tags as well as having working in optimization on ecological data sets. Dr. Ashlock is an Associate Editor of the IEEE/ACM Transactions on Computational Biology and Bioinformatics. He is a longstanding member of the IEEE Bioinformatics and Bioengineering Technical Committee, and has served as General Chair of CIBCB twice.
Dr Sansanee Auephanwiriyakul is an Associate Professor in the Department of Computer Engineering, Chiang Mai University, Thailand. She is also a joint professor in the Biomedical Engineering Program at the same university. She is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). She is an Associate Editor of the IEEE Transactions on Fuzzy System, and on the Editorial Board of Neural Computing and Applications, the International Journal of Computational & Neural Engineering, and the Engineering Journal Chiang Mai University. She was a Guest Editor of the Journal of Advanced Computational Intelligence and Intelligent Informatics in 2004. Her main research interest is in Computational Intelligence Theory and Application especially in Fuzzy Set Theory Application in Biomedical Engineering and Pattern Recognition.