AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: MARIA-IULIANA BOCICOR, ALESSANDRO PANDINI, GABRIELA CZIBULA, SILVANA ALBERT, MIHAI TELETIN.
DOI: 10.24193/subbi.2017.1.08
Published Online: 2017-06-01
Published Print: 2017-06-01
pp. 107-120
VIEW PDF: USING COMPUTATIONAL INTELLIGENCE MODELS FOR ADDITIONAL INSIGHT INTO PROTEIN STRUCTURE
Proteins are large, complex molecules with crucial roles in the functioning of living organisms. Understanding the underlying mechanisms by which proteins achieve their structures and substructures, as well as those involved in the conformational transitions may contribute to a deeper comprehension of the involved biological processes. This paper investigates a new machine learning perspective upon analyzing protein conformational transitions and introduces a new formalization for the problem, with the more general goal of uncovering interesting patterns in protein conformational transitions. This study represents the starting point of a research which is being conducted in order to obtain a better comprehension of proteins structures and, implicitly, functions, by investigating computational intelligence methods for analyzing and deducing proteins conformational transitions.
2010 Mathematics Subject Classification. 68T05, 62H30.1998 CR Categories and Descriptors. I.2.6 [Computing Methodologies]: Artificial Intelligence { Learning; I.5.3 [Computing Methodologies]: Pattern Recognition { Clustering.
Key words and phrases. Protein conformations, Computational Intelligence, Machinelearning, Self-organizing maps.
