Special Session on Optimization Methods for Bioinformatics and Bioengineering (OMBB)¶
IEEE Congress on Evolutionary Computation 2021 (IEEE CEC 2021), June 28 – July 1 2021, Krakow Poland
Overview¶
Bioinformatics and Bioengineering are interdisciplinary scientific fields involving many branches of computer science, engineering, mathematics, biology, clinical applications, and statistics. Bioinformatics is concerned with the development and application of computational methods for the modeling, retrieval 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 or 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 newcomers working on Optimization Methods in Bioinformatics and Bioengineering to discuss new and exciting issues, and explore cutting edge advancements in this field.
We encourage submission of papers describing new optimization strategies / challenges / applications / decision-making techniques in this area. While we are interested in fundamental algorithmic advancements in this area, we emphasize that we also welcome application papers discussing the power and applicability of these methods to real-world problems in Bioinformatics and Bioengineering. You are invited to submit papers that are unpublished original work for this special session at IEEE CEC2021.
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).
Topics of Interest¶
- Single and multiobjective) optimization techniques for Bioinformatics and Bioengineering problems
Evolutionary algorithms
Swarm Intelligence
Metaheuristics
Hybrid optimization algorithms (combinations of heuristics and exact methods)
- Decision-making techniques for Bioinformatics and Bioengineering problems
Systems BiologyPreference elicitation and representation
Systems BiologyAggregation-based techniques
- Experimental optimization of Bioinformatics and Bioengineering problems
Systems BiologyExperimental optimization platforms
Systems BiologyClosed-loop optimization challenges and applications
Systems BiologyResourcing issues (interruptions, missing objective function values, changes of variables, etc.)
Systems BiologyTreatment optimization
- Learning in the optimization of Bioinformatics and Bioengineering problems
Systems BiologyLink between Decision Maker’s learning and model’s learning
Systems BiologyCapturing and learning from user preferences
Systems BiologyIntegrating optimization with machine learning
Systems BiologyInteractive learning and optimization techniques
Systems BiologyTechniques for learning user-driven parameter settings from examples
- Tuning of optimization and decision-making techniques for Bionformatics and Bioengineering problems
Systems BiologyPerformance measures
Systems BiologyTest and benchmark problems
Systems BiologyVisualization techniques
Systems BiologyOptimization and visualization software
- Emerging topics in Bioinformatics and Bioengineering
Systems BiologyNovel applications (process design, manufacturing, etc)
Systems BiologyNovel challenges (large-scale problems, dynamic problems, mixed integer problems, uncertainty, expensive and limited evaluations, etc)
Systems BiologyBilevel and multilevel optimization
Systems BiologyInteractive visualization techniques/Analysis and visualization of large biological data sets
Systems BiologyMultiobjective data mining
Systems BiologyPredictive fitness landscape design
Systems BiologyMany-objective optimization
Systems BiologyEcoinformatics
Systems BiologySide effect machines and other kernal representations for sequence analysis
Systems BiologyBiomedical data modelling and mining
- Applications
Brain computer interface
Computational proteomics
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
Systems Biology
Visualization and analysis of single-cell RNA-seq data
Biological network inference using omics data
Paper Submission¶
The Special Session on Optimization Methods for Bioinformatics and Bioengineering (OMBB) 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 CEC 2021 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 CEC 2021 conference proceedings.
Important Dates¶
Please check IEEE CEC 2021 website for up-to-date information.
Paper Submission: Jauary 31, 2021
Notification of Acceptance: March 22, 2021
Submission of Accepted Papers: April 7, 2021
Organizers¶
Sheridan Houghten is a professor in the department of Computer Science at Brock University, where she has also served terms as department chair and as graduate program director. She is currently chair of the IEEE Computational Intelligence Society Bioinformatics and Bioengineering Technical Committee, and is also a member of the IEEE Computational Intelligence Society Task Force on Ethical and Social Implications of Computational Intelligence. Her research interests include combinatorial optimization, computational intelligence, and algorithms (in the general sense). Her research is directed at various application areas, including bioinformatics and information theory.
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.