IEEE Congress on Evolutionary Computation (IEEE CEC 2016)
25-29 July, 2016, Vancouver, Canada

Special Session Title: Evolutionary Computation for Computational Biology (ID: CEC-30)

Organized by: Dr. Anirban Mukhopadhyay, Dr. Ujjwal Maulik and Dr. Sanghamitra Bandyopadhyay

Aim and Objective 

Computational biology is coming out as an emerging field for application of evolutionary computation tools and techniques such as genetic algorithms, genetic programming, differential evolution, particle swarm optimization, ant colony optimization and other related population-based metaheuristic techniques. Many of the computational biology problems, such as sequence alignment, gene mapping, fragment assembly, phylogenetic analysis, microarray analysis, biological network analysis and rational drug design can be posed as optimization problems. Therefore evolutionary computing techniques have been applied to these problems over the last few decades as optimization tools. However, growing size and complexity of biological data are creating new issues and challenges and it is becoming difficult to apply off-the-shelf techniques directly. These challenges include coping with large data size, handling many objective functions, dealing with large number of features, incorporating biological knowledge in the models etc. The main aim of this special session is to bring together the scientists and researchers of this field to exchange the latest advances in theories and experiments in this area.

Scope and Topics

Researchers are invited to submit original and unpublished works that deal with application of evolutionary computation techniques to the following and other related areas.
  • Sequence analysis including next-generation sequencing (sequence alignment, fragment assembly, gene mapping etc.).
  • Structure prediction (RNA and protein structure prediction, protein folding).
  • Microarray analysis (clustering, classification, feature selection etc.).
  • Genetic marker identification (cancer and other diseases).
  • Bio-molecule ordering and rank aggregation.
  • Protein-protein interaction prediction (intra-species and host-pathogen interactions).
  • Protein complex identification.
  • Protein sub-cellular location prediction.
  • Inferring gene regulatory and metabolic networks (including involvement of microRNAs).
  • Phyologenetic analysis and phylogenetic tree construction.
  • MicroRNA target prediction.
  • Drug target identification.
  • Differential network analysis and biological network alignment.
  • Biological motif finding (sequence and network motifs).
  • Rational drug design (molecular docking, ligand design etc.).
  • Multi-objective and many-objective optimization for computational biology problems.
  • Parallelization of evolutionary computing techniques for handling large biological data.

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