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Fine-grained parallelization of fitness functions in bioinformatics optimization problems: Gene selection for cancer classification and biclustering of gene expression data

dc.contributor.authorGomez-Pulido J.A.
dc.contributor.authorCerrada-Barrios J.L.
dc.contributor.authorTrinidad-Amado S.
dc.contributor.authorLanza-Gutierrez J.M.
dc.contributor.authorFernandez-Diaz R.A.
dc.contributor.authorCrawford B.
dc.contributor.authorSoto R.
dc.date.accessioned2020-09-02T22:19:20Z
dc.date.available2020-09-02T22:19:20Z
dc.date.issued2016
dc.identifier10.1186/s12859-016-1200-9
dc.identifier.citation17, 1, -
dc.identifier.issn14712105
dc.identifier.urihttps://hdl.handle.net/20.500.12728/4707
dc.descriptionBackground: Metaheuristics are widely used to solve large combinatorial optimization problems in bioinformatics because of the huge set of possible solutions. Two representative problems are gene selection for cancer classification and biclustering of gene expression data. In most cases, these metaheuristics, as well as other non-linear techniques, apply a fitness function to each possible solution with a size-limited population, and that step involves higher latencies than other parts of the algorithms, which is the reason why the execution time of the applications will mainly depend on the execution time of the fitness function. In addition, it is usual to find floating-point arithmetic formulations for the fitness functions. This way, a careful parallelization of these functions using the reconfigurable hardware technology will accelerate the computation, specially if they are applied in parallel to several solutions of the population. Results: A fine-grained parallelization of two floating-point fitness functions of different complexities and features involved in biclustering of gene expression data and gene selection for cancer classification allowed for obtaining higher speedups and power-reduced computation with regard to usual microprocessors. Conclusions: The results show better performances using reconfigurable hardware technology instead of usual microprocessors, in computing time and power consumption terms, not only because of the parallelization of the arithmetic operations, but also thanks to the concurrent fitness evaluation for several individuals of the population in the metaheuristic. This is a good basis for building accelerated and low-energy solutions for intensive computing scenarios. © 2016 The Author(s).
dc.language.isoen
dc.publisherBioMed Central Ltd.
dc.subjectBiclustering
dc.subjectCancer classification
dc.subjectFitness function
dc.subjectFloating-point arithmetic
dc.subjectFPGA
dc.subjectMetaheuristics
dc.subjectParallelism
dc.subjectBioinformatics
dc.subjectClassification (of information)
dc.subjectCombinatorial optimization
dc.subjectComputer aided diagnosis
dc.subjectComputer hardware
dc.subjectDigital arithmetic
dc.subjectDiseases
dc.subjectField programmable gate arrays (FPGA)
dc.subjectGenes
dc.subjectHardware
dc.subjectHealth
dc.subjectHeuristic algorithms
dc.subjectHeuristic programming
dc.subjectOptimization
dc.subjectParallel processing systems
dc.subjectReconfigurable hardware
dc.subjectBi-clustering
dc.subjectCancer classification
dc.subjectFitness functions
dc.subjectMeta heuristics
dc.subjectParallelism
dc.subjectGene expression
dc.subjectalgorithm
dc.subjectbiology
dc.subjectclassification
dc.subjectgene expression regulation
dc.subjectgenetics
dc.subjecthuman
dc.subjectneoplasm
dc.subjectpathology
dc.subjectprocedures
dc.subjectsoftware
dc.subjectAlgorithms
dc.subjectComputational Biology
dc.subjectGene Expression Regulation, Neoplastic
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectSoftware
dc.titleFine-grained parallelization of fitness functions in bioinformatics optimization problems: Gene selection for cancer classification and biclustering of gene expression data
dc.typeArticle


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