Computational Intelligence for Life Sciences

Issue title: Interdisciplinary Nature of Information Processing Special Issue Dedicated to Giancarlo Mauri on the Occasion of His 70th Birthday

Guest editors: Alberto Dennunzio, Gheorghe Păun, Grzegorz Rozenberg and Claudio Zandron

Article type: Research Article

Authors: Besozzi, Daniela^{a; *} | Manzoni, Luca^a | Nobile, Marco S.^a | Spolaor, Simone^a | Castelli, Mauro^b | Vanneschi, Leonardo^b | Cazzaniga, Paolo^{c; †} | Ruberto, Stefano^{d; ‡} | Rundo, Leonardo^{e; §} | Tangherloni, Andrea^{f; £}

Affiliations: [a] Department of Informatics, University of Milano - Bicocca, Milano, Italy. daniela.besozzi@unimib.it | [b] NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal | [c] Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy | [d] Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, PA, USA | [e] Department of Radiology, University of Cambridge, Cambridge, UK | [f] Department of Haematology, University of Cambridge, Cambridge, UK

Correspondence: [*] Address for correspondence: Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.

Note: [†] Also affiliated at: SYSBIO.IT Centre of Systems Biology, Milano, Italy.

Note: [‡] Also affiliated at: GSSI, Gran Sasso Science Institute, INFN, L’Aquila, Italy.

Note: [§] Also affiliated at: Cancer Research UK Cambridge Centre, Cambridge, UK.

Note: [£] Also affiliated at: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.

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, Swarm Intelligence, Genetic Programming, Genetic Algorithm, Particle Swarm Optimization, Protein Folding, Haplotype Assembly, Parameter Estimation

DOI: 10.3233/FI-2020-1872

Journal: Fundamenta Informaticae, vol. 171, no. 1-4, pp. 57-80, 2020