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On the role of metaheuristic optimization in bioinformatics

Authors: Laura Calvet; Sergio Benito; Angel A. Juan; Ferran Prados;

On the role of metaheuristic optimization in bioinformatics

Abstract

AbstractMetaheuristic algorithms are employed to solve complex and large‐scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.

Countries
Spain, United Kingdom
Keywords

metaheuristics, metaheurísticas, Combinatorial optimization, Bioinformatics, ESTADISTICA E INVESTIGACION OPERATIVA, 006, bioinformatics, Metaheuristics, optimización combinatoria, bioinformática, optimització combinatòria, metaheuristiques, combinatorial optimization, bioinformàtica, Operations research, mathematical programming

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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22
Top 10%
Average
Top 10%
44
95
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hybrid