Computational Intelligence for Life Sciences

• Autorzy: Besozzi Daniela , Manzoni Luca , Nobile Marco S. , Spolaor Simone , Castelli Mauro , Vanneschi Leonardo , Cazzaniga Paolo , Ruberto Stefano , Rundo Leonardo , Tangherloni Andrea

Identyfikatory

DOI

10.3233/FI-2020-1872

• Języki publikacji: EN

Abstrakty

EN

Computational 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.

Słowa kluczowe

EN

computational intelligence; evolutionary computation; genetic algorithm; genetic programming; haplotype assembly; parameter estimation; particle swarm optimization; PSO; protein folding; swarm intelligence

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2020
• Tom: Vol. 171, nr 1-4
• Strony: 57--80
• Opis fizyczny: Bibliogr. 107 poz., rys., tab., wykr.

Twórcy

autor

Besozzi Daniela

• Department of Informatics, University of Milano - Bicocca, Milano, Italy

autor

Manzoni Luca

• Department of Informatics, University of Milano - Bicocca, Milano, Italy

autor

Nobile Marco S.

• Department of Informatics, University of Milano - Bicocca, Milano, Italy

autor

Spolaor Simone

• Department of Informatics, University of Milano - Bicocca, Milano, Italy

autor

Castelli Mauro

• NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal

autor

Vanneschi Leonardo

• NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal

autor

Cazzaniga Paolo

• Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy
• SYSBIO.IT Centre of Systems Biology, Milano, Italy

autor

Ruberto Stefano

• Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, PA, USA
• GSSI, Gran Sasso Science Institute, INFN, L’Aquila, Italy

autor

Rundo Leonardo

• Department of Radiology, University of Cambridge, Cambridge, UK
• Cancer Research UK Cambridge Centre, Cambridge, UK

autor

Tangherloni Andrea

• Department of Haematology, University of Cambridge, Cambridge, UK
• Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).