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IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering (OMBB)

Mission

Our aim is to promote research on the design, application and theory of optimization methods in the field of bioinformatics and bioengineering.

What is Bioinformatics and Bioengineering?

Bioinformatics and Bioengineering 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. There is a long list of problems in the field that can be formulated as optimization problems including, just to name a few, the optimization of biochemical processes, protein structure alignment and prediction, protein sequence alignment, directed evolution, drug design and discovery, and experimental design.

Whilst Bioinformatics uses computers to understand biology better, Bioengineering is the application of engineering techniques to biology so as to create usable and economically viable products. Typical problems in the field deal with the design of biological systems, optimization of manufacturing processes, material and equipment optimization, and biomedical equipment manufacturing.

Why a Task Force Group on Optimization Methods in Bioinformatics and Bioengineering?

Bioinformatics and Bioengineering are relatively new fields subject to different types of challenging optimization problems, urging the need for effective optimization methods due to several reasons:

  • Biological data sets are typically complex, very large, include noisy measurements, and may change over time, thus requiring advanced optimization approaches.

  • Problems can often be formulated as multiobjective optimization problems, and thus require optimization methods capable of discovering a set of trade-off solutions (i.e. Pareto optimal solutions) rather than a single solution.

  • Problems may involve time-consuming and expensive physical experiments and/or computer simulations, and thus require optimization methods that arrive at a robust solution as quickly as possible, whilst, potentially, needing to account for constraints, uncertainty, mixed-variable types, user preferences, and various resourcing issues.

  • Problems can be “black box” (i.e. lack gradient information) and thus call for derivative-free optimization methods, such as nature-inspired algorithms and heuristics.

Start Learning about Optimization Methods in/and Bioinformatics and Bioengineering

Introductions to Bioinformatics and Bioengineering have been given by:

A tutorial on optimization in Bioinformatics can be found at:

Introductions to (multiobjective) optimization methods and their application to various problems in Bioinformatics and Bioengineering have been given by:

If you want to test your own optimization method on Bioinformatics problems, then datasets as well as Bioinformatics software can be found at:

Literature focused in more detail on the topics evolutionary multiobjective optimization, evolutionary optimization in dynamic environments, and resourcing issues in experimental optimization include:

Future Events

Future events planned by our Task Force:

Other important upcoming events related to optimization methods in Bioinformatics and Bioengineering are the following:

Past Events

  • IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology 2020 (CIBCB 2020) in Vina del Mar, Chile

  • IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2019) in Siena, Tuscany, Italy

  • IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2018) in Saint Louis, Missouri, USA

  • IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2017) in Manchester, UK

  • Special Session on Data-Driven Multiple Criteria Decision Making at the 24th International Conference on Multiple Criteria Decision Making (MCDM 2017) in Ottawa, Ontario, Canada

  • Special Session on Optimization, Learning, and Decision-Making in Bioinformatics and Bioengineering (OLDBB) at the IEEE World Congress on Computational Intelligence 2016 (WCCI 16) in Vancouver, British Columbia, Canada

  • Special Session on Multiobjective Optimization in Bioinformatics, Computational Biology and Biomedical Engineering at the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology 2016 (CIBCB 2016) in Chiang Mai, Thailand

  • IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology 2015 (CIBCB 15) in Niagara Falls, Ontario, Canada

  • Special Session on Optimization Methods in Bioinformatics and Bioengineering at the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology 2015 (CIBCB 15) in Niagara Falls, Ontario, Canada

  • Special Session on Non-Standard Multiobjective Problems at the 27th European Conference on Operational Research (EURO 15) in Glasgow, Scotland, UK

  • Special Session on Multiobjective Optimization Methods in Bioinformatics and Bioengineering at the IEEE Symposium Series on Computational Intelligence (SSCI 14) in Orlando, Florida, USA

  • IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology 2014 (CIBCB 14) in Honolulu, Hawaii, USA

The IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology consistenyl represents related work.

Contact

If you want to know more about the activities of this working group, or you want to join us, please contact:

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Dr. James Hughes
Assistant Professor, Alley Heaps Associate Chair, Dr WF James Scholar
Computer Science Department
St. Francis Xavier University
Antigonish, Nova Scotia, Canada

James Hughes is an assistant professor, Alley Heaps Associate Chair, and Dr. WF James Chair (Scholar) in Pure and Applied Sciences at St. Francis Xavier University. James’ interests include the development of machine learning algorithms for real world applications. Although he is interested in many algorithms, particular strategies of interest are Evolutionary Computation and Artificial Neural Networks. Application areas include neuroinformatics, bioinformatics, kinematics, geology, art, finance, and clinical applications.

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Dr. Steven Corns
Associate Professor
Engineering Management and Systems Engineering Department
Missouri University of Science and Technology
Rolla, Missouri, USA

Steven Corns is an Associate Professor in the Engineering Management and Systems Engineering Department at Missouri University of Science and Technology. Steven is an investigator in the Environmental Research Center for Emerging Contaminants and the Energy Research and Development Center at Missouri S&T. He also serves as Vice Chair on the Bioinformatics and Bioengineering Technical Committee for the IEEE Computational Intelligence Society, represent the IEEE Copmutational Intelligence Society as chair for the Greater St. Louis Area, and is the lead investigator for the INCOSE MBSE Initiative Biomedical Challenge Team.