TY - JOUR
T1 - Computational Intelligence for Life Sciences
AU - Besozzi, Daniela
AU - Manzoni, Luca
AU - Nobile, Marco S.
AU - Spolaor, Simone
AU - Castelli, Mauro
AU - Vanneschi, Leonardo
AU - Cazzaniga, Paolo
AU - Ruberto, Stefano
AU - Rundo, Leonardo
AU - Tangherloni, Andrea
N1 - info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT#
info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT#
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
PY - 2020/1/1
Y1 - 2020/1/1
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.
AB - 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.
KW - Computational Intelligence
KW - Evolutionary Computation
KW - Genetic Algorithm
KW - Genetic Programming
KW - Haplotype Assembly
KW - Parameter Estimation
KW - Particle Swarm Optimization
KW - Protein Folding
KW - Swarm Intelligence
UR - http://www.scopus.com/inward/record.url?scp=85075866558&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000509413400005
U2 - 10.3233/FI-2020-1872
DO - 10.3233/FI-2020-1872
M3 - Article
AN - SCOPUS:85075866558
SN - 0169-2968
VL - 171
SP - 57
EP - 80
JO - Fundamenta Informaticae
JF - Fundamenta Informaticae
IS - 1-4
ER -
