Computational Intelligence for Life Sciences

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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)57-80
Number of pages24
JournalFundamenta Informaticae
Volume171
Issue number1-4
DOIs
Publication statusPublished - 1 Jan 2020

Fingerprint

Computational Intelligence
Life sciences
Artificial intelligence
Swarm Intelligence
Protein folding
Protein Folding
Haplotype
Genetic programming
Evolutionary Computation
Genetic Programming
Kinetic parameters
Evolutionary algorithms
Computer science
Fuzzy Logic
Fuzzy logic
Computer Science
Genetic algorithms
Kinetics
Paradigm
Genetic Algorithm

Keywords

  • Computational Intelligence
  • Evolutionary Computation
  • Genetic Algorithm
  • Genetic Programming
  • Haplotype Assembly
  • Parameter Estimation
  • Particle Swarm Optimization
  • Protein Folding
  • Swarm Intelligence

Cite this

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
Besozzi, Daniela ; Manzoni, Luca ; Nobile, Marco S. ; Spolaor, Simone ; Castelli, Mauro ; Vanneschi, Leonardo ; Cazzaniga, Paolo ; Ruberto, Stefano ; Rundo, Leonardo ; Tangherloni, Andrea. / Computational Intelligence for Life Sciences. In: Fundamenta Informaticae. 2020 ; Vol. 171, No. 1-4. pp. 57-80.
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Besozzi, D, Manzoni, L, Nobile, MS, Spolaor, S, Castelli, M, Vanneschi, L, Cazzaniga, P, Ruberto, S, Rundo, L & Tangherloni, A 2020, 'Computational Intelligence for Life Sciences', Fundamenta Informaticae, vol. 171, no. 1-4, pp. 57-80. https://doi.org/10.3233/FI-2020-1872

Computational Intelligence for Life Sciences. / Besozzi, Daniela; Manzoni, Luca; Nobile, Marco S.; Spolaor, Simone; Castelli, Mauro; Vanneschi, Leonardo; Cazzaniga, Paolo; Ruberto, Stefano; Rundo, Leonardo; Tangherloni, Andrea.

In: Fundamenta Informaticae, Vol. 171, No. 1-4, 01.01.2020, p. 57-80.

Research output: Contribution to journalArticle

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AU - Cazzaniga, Paolo

AU - Ruberto, Stefano

AU - Rundo, Leonardo

AU - Tangherloni, Andrea

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N2 - 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.

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