Computational Intelligence for life sciences

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

doi:10.3233/FI-2020-1872

Computational Intelligence for life sciences

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

Information Systems IE&IS

Research output: Contribution to journal › Article › Academic › peer-review

6 Citations (Scopus)

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 language	English
Pages (from-to)	57-80
Number of pages	24
Journal	Fundamenta Informaticae
Volume	171
Issue number	1-4
DOIs	https://doi.org/10.3233/FI-2020-1872
Publication status	Published - 23 Oct 2019

Keywords

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

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10.3233/FI-2020-1872

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title = "Computational Intelligence for life sciences",

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. ",

keywords = "Computational Intelligence, Evolutionary Computation, Genetic Algorithm, Genetic Programming, Haplotype Assembly, Parameter Estimation, Particle Swarm Optimization, Protein Folding, Swarm Intelligence",

author = "Daniela Besozzi and Mauro Castelli and Paolo Cazzaniga and Luca Manzoni and Nobile, {Marco S.} and Stefano Ruberto and Leonardo Rundo and Simone Spolaor and Andrea Tangherloni and Leonardo Vanneschi",

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doi = "10.3233/FI-2020-1872",

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AU - Besozzi, Daniela

AU - Castelli, Mauro

AU - Cazzaniga, Paolo

AU - Manzoni, Luca

AU - Nobile, Marco S.

AU - Ruberto, Stefano

AU - Rundo, Leonardo

AU - Spolaor, Simone

AU - Tangherloni, Andrea

AU - Vanneschi, Leonardo

PY - 2019/10/23

Y1 - 2019/10/23

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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JF - Fundamenta Informaticae

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Computational Intelligence for life sciences

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Keywords

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