Computational Intelligence for Life Sciences

Detalhes bibliográficos
Autor(a) principal: Besozzi, Daniela
Data de Publicação: 2020
Outros Autores: Manzoni, Luca, Nobile, Marco S., Spolaor, Simone, Castelli, Mauro, Vanneschi, Leonardo, Cazzaniga, Paolo, Ruberto, Stefano, Rundo, Leonardo, Tangherloni, Andrea
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/95133
Resumo: Besozzi, D., Manzoni, L., Nobile, M. S., Spolaor, S., Castelli, M., Vanneschi, L., ... Tangherloni, A. (2020). Computational Intelligence for Life Sciences. Fundamenta Informaticae, 171(1-4), 57-80. https://doi.org/10.3233/FI-2020-1872
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spelling Computational Intelligence for Life SciencesComputational IntelligenceEvolutionary ComputationGenetic AlgorithmGenetic ProgrammingHaplotype AssemblyParameter EstimationParticle Swarm OptimizationProtein FoldingSwarm IntelligenceTheoretical Computer ScienceAlgebra and Number TheoryInformation SystemsComputational Theory and MathematicsSDG 3 - Good Health and Well-beingBesozzi, D., Manzoni, L., Nobile, M. S., Spolaor, S., Castelli, M., Vanneschi, L., ... Tangherloni, A. (2020). Computational Intelligence for Life Sciences. Fundamenta Informaticae, 171(1-4), 57-80. https://doi.org/10.3233/FI-2020-1872Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBesozzi, DanielaManzoni, LucaNobile, Marco S.Spolaor, SimoneCastelli, MauroVanneschi, LeonardoCazzaniga, PaoloRuberto, StefanoRundo, LeonardoTangherloni, Andrea2020-03-26T23:35:07Z2020-01-012020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article24application/pdfhttp://hdl.handle.net/10362/95133eng0169-2968PURE: 15891263https://doi.org/10.3233/FI-2020-1872info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:43:09Zoai:run.unl.pt:10362/95133Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:38:14.017978Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Computational Intelligence for Life Sciences
title Computational Intelligence for Life Sciences
spellingShingle Computational Intelligence for Life Sciences
Besozzi, Daniela
Computational Intelligence
Evolutionary Computation
Genetic Algorithm
Genetic Programming
Haplotype Assembly
Parameter Estimation
Particle Swarm Optimization
Protein Folding
Swarm Intelligence
Theoretical Computer Science
Algebra and Number Theory
Information Systems
Computational Theory and Mathematics
SDG 3 - Good Health and Well-being
title_short Computational Intelligence for Life Sciences
title_full Computational Intelligence for Life Sciences
title_fullStr Computational Intelligence for Life Sciences
title_full_unstemmed Computational Intelligence for Life Sciences
title_sort Computational Intelligence for Life Sciences
author Besozzi, Daniela
author_facet Besozzi, Daniela
Manzoni, Luca
Nobile, Marco S.
Spolaor, Simone
Castelli, Mauro
Vanneschi, Leonardo
Cazzaniga, Paolo
Ruberto, Stefano
Rundo, Leonardo
Tangherloni, Andrea
author_role author
author2 Manzoni, Luca
Nobile, Marco S.
Spolaor, Simone
Castelli, Mauro
Vanneschi, Leonardo
Cazzaniga, Paolo
Ruberto, Stefano
Rundo, Leonardo
Tangherloni, Andrea
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Besozzi, Daniela
Manzoni, Luca
Nobile, Marco S.
Spolaor, Simone
Castelli, Mauro
Vanneschi, Leonardo
Cazzaniga, Paolo
Ruberto, Stefano
Rundo, Leonardo
Tangherloni, Andrea
dc.subject.por.fl_str_mv Computational Intelligence
Evolutionary Computation
Genetic Algorithm
Genetic Programming
Haplotype Assembly
Parameter Estimation
Particle Swarm Optimization
Protein Folding
Swarm Intelligence
Theoretical Computer Science
Algebra and Number Theory
Information Systems
Computational Theory and Mathematics
SDG 3 - Good Health and Well-being
topic Computational Intelligence
Evolutionary Computation
Genetic Algorithm
Genetic Programming
Haplotype Assembly
Parameter Estimation
Particle Swarm Optimization
Protein Folding
Swarm Intelligence
Theoretical Computer Science
Algebra and Number Theory
Information Systems
Computational Theory and Mathematics
SDG 3 - Good Health and Well-being
description Besozzi, D., Manzoni, L., Nobile, M. S., Spolaor, S., Castelli, M., Vanneschi, L., ... Tangherloni, A. (2020). Computational Intelligence for Life Sciences. Fundamenta Informaticae, 171(1-4), 57-80. https://doi.org/10.3233/FI-2020-1872
publishDate 2020
dc.date.none.fl_str_mv 2020-03-26T23:35:07Z
2020-01-01
2020-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/95133
url http://hdl.handle.net/10362/95133
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0169-2968
PURE: 15891263
https://doi.org/10.3233/FI-2020-1872
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 24
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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