DC Programming and DCA for Challenging Problems in Bioinformatics and Computational Biology

  • Le Thi Hoai AnEmail author
Part of the Emergence, Complexity and Computation book series (ECC, volume 12)


Nonconvex optimization is a powerful tool in bioinformatics and computational biology. As an innovative approach to nonconvex programming, Difference of Convex functions (DC) programming and DC Algorithms (DCA) have proved to be efficient for several problems in computational biology. The objective of this chapter is to show that grand challenge problems in bioinformatics and computational biology can be modeled as DC programs and solved by DCA based algorithms. We offer the community of researchers in computational biology promising approaches in a unified DC programming framework to tackle challenging biological problems such as Multiple Alignment of Sequence (MSA), Molecular conformation and Phylogenetic tree reconstruction.


Polyhedral Convex Distance Geometry Mixed Graph Phylogenetic Tree Reconstruction Distance Geometry Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Laboratory of Theoretical and Applied Computer Science (LITA EA 3097)University of LorraineMetzFrance

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