Bioinformatics and Bioengineering (BB) are interdisciplinary scientific fields involving many branches of computer science, engineering, mathematics, and statistics. Bioinformatics is concerned with the development and application of computational methods for the modeling, retrieving and analysis of biological data, whilst Bioengineering is the application of engineering techniques to biology so as to create usable and economically viable products.

Bioinformatics and Bioengineering are relatively new fields in which many challenges and issues can be formulated as (single and multiobjective) optimization problems. These problems span from traditional problems, such as the optimization of biochemical processes, construction of gene regulatory networks, protein structure alignment and prediction, to more modern problems, such as directed evolution, drug design, experimental design, and optimization of manufacturing processes, material and equipment.

The main aim of this special session is to bring together both experts and new-comers working on Optimization, Learning and Decision-Making in Bioinformatics and Bioengineering to discuss new and exciting issues in this area.

We encourage submission of papers describing new optimization strategies / challenges / applications / decision-making techniques in the area of BB. In addition, we are interested in application papers discussing the power and applicability of these novel methods to real-world problems in BB. You are invited to submit papers that are unpublished original work for this special session at IEEE WCCI2020.

This session is supported by the IEEE CIS Bioinformatics and Bioengineering Technical Committee (BBTC) and the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering (OMBB).

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Topics of interest

  • (Single and multiobjective) optimization techniques for Bioinformatics and Bioengineering (BB) problems
  • Evolutionary algorithms
  • Swarm Intelligence
  • Metaheuristics
  • Fuzzy optimization
  • Hybrid optimization algorithms (combinations of heuristics and exact methods)

  • Decision-making techniques for BB problems
  • Preference elicitation and representation
  • Aggregation-based techniques
  • Fuzzy logic-based techniques
  • Bayesian-based techniques

  • Experimental optimization of BB problems
  • Experimental optimization platforms
  • Closed-loop optimization challenges and applications
  • Resourcing issues (interruptions, missing objective function values, changes of variables, etc.)
  • Treatment optimization

  • Learning in the optimization of BB problems
  • Link between Decision Maker’s learning and model’s learning
  • Capturing and learning from user preferences
  • Integrating optimization with machine learning
  • Interactive learning and optimization techniques
  • Techniques for learning user-driven parameter settings from examples

  • Tuning of optimization and decision-making techniques for BB problems
  • Performance measures
  • Test and benchmark problems
  • Visualization techniques
  • Optimization and visualization software

  • Emerging topics in BB
  • Novel applications (process design, manufacturing, etc)
  • Novel challenges (large-scale problems, dynamic problems, mixed integer problems, uncertainty, expensive and limited evaluations, etc)
  • Bilevel and multilevel optimization
  • Interactive visualization techniques/Analysis and visualization of large biological data sets
  • Multiobjective data mining
  • Predictive fitness landscape design
  • Many-objective optimization
  • Ecoinformatics
  • Side effect machines and other kernal representations for sequence analysis
  • Biomedical data modelling and mining

  • Applications
  • Systems biology
  • Brain computer interface
  • Computational proteomics
  • Systems Biology
  • Emergent properties in complex biological systems
  • Gene expression array analysis
  • Gene finding
  • Genetic networks
  • High-throughput data analysis
  • Immuno- and chemo-informatics
  • In-silico optimization of biological systems
  • Medical image analysis
  • Medical imaging and pattern recognition
  • Medicine and health informatics
  • Metabolic pathway analysis
  • Microarray design or oligonucleotide selection
  • Molecular docking and drug design
  • Molecular evolution and phylogenetics
  • Molecular sequence alignment and analysis
  • Motif and signal detection
  • Robustness and evolvability of biological networks
  • Single nucleotide polymorphism (SNP) analysis
  • Structure prediction and folding
  • Visualization and analysis of single-cell RNA-seq data
  • Biological network inference using omics data

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Paper Submission:

The Special Session on Optimization, Learning, and Decision-Making in Bioinformatics and Bioengineering (OLDMBB) is soliciting high quality papers of original research and application papers that have not been published elsewhere and are not under consideration for publication elsewhere. Manuscripts should be prepared according to the standard format and page limit of regular papers specified in the IEEE WCCI 2020 website. All papers will be rigorously reviewed by at least two reviewers. Accepted papers will be treated in the same way as regular papers and published in the IEEE WCCI 2020 conference proceedings.

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Important Dates (please also check IEEE WCCI 2020 website for up-to-date information):

  • Paper Submission: January 30, 2020
  • Notification of acceptance: March 15, 2020
  • Submission of Accepted Papers: April 15, 2020

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  • Richard Allmendinger, Alliance Manchester Business School, The University of Manchester, UK
  • Vassilis Plagianakos, Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece.

About the Organizers:

Richard Allmendinger is a Senior Lecturer in Data Science at The University of Manchester and Business Engagement Lead of Alliance Manchester Business School. Prior to Manchester, he worked at the Biochemical Engineering Department, University College London. He studied Business Engineering at the Karlsruhe Institute of Technology and the Royal Melbourne Institute of Technology and completed a PhD in Computer Science at The University of Manchester. Richard's research interests are in the field of data science and in particular in the development and application of optimization, learning and analytics techniques to real-world problems arising in areas such as healthcare and manufacturing. Much of his research has been funded by grants from Innovate UK, the Engineering and Physical Sciences Research Council (EPSRC), and industrial partners. Richard is the Co-Founder and Vice-Chair of the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering, a Member of the IEEE CIS Bioinformatics and Bioengineering Technical Committee, the General Chair of the 2017 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, and a Member of the Editorial Board of the Applied Soft Computing journal.
Web: https://personalpages.manchester.ac.uk/staff/Richard.Allmendinger/

Vassilis P. Plagianakos is a Professor of the Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece and the President of the Hellenic National Organization for the Provision of Health Services (E.O.P.Y.Y.). He has served as the Head of the Department of Computer Science and Biomedical Informatics (2014-2019) and the Director of the postgraduate program Informatics and Computational Biomedicine of the School of Sciences (2014-2019). He is also the Founder of the Intelligent Systems Laboratory of the Department of Computer Science and Biomedical Informatics. Prof. Plagianakos is coauthor of more than 40 journal publications and more than 90 conference papers, and his published work has received more than 3000 citations. He has served as the Chair of the Board of the Hellenic Artificial Intelligence Society (2017-2019) and is a member of the IEEE Neural Networks Society and the IEEE Bioinformatics and Bioengineering Technical Committee (BBTC). His research interests are in the areas of Machine Learning and Neural Networks, Evolutionary and Genetic Algorithms, Intelligent decision making, Data Mining, Bioinformatics, Clustering, Parallel and Distributed computations, and real-world problem solving. He was the co-organizer of the bioinformatics hybrid special sessions at WCCI 2012, WCCI2014 and WCCI 2016.
Web: https://www.plagianakos.gr/

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