dc.creator | Márquez Chamorro, Alfonso Eduardo | es |
dc.creator | Asencio Cortés, Gualberto | es |
dc.creator | Santiesteban Toca, Cosme E. | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.date.accessioned | 2022-05-24T06:41:35Z | |
dc.date.available | 2022-05-24T06:41:35Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Márquez Chamorro, A.E., Asencio Cortés, G., Santiesteban Toca, C.E. y Aguilar Ruiz, J.S. (2015). Soft computing methods for the prediction of protein tertiary structures: A survey. Applied Soft Computing, 35 (October 2015), 398-410. | |
dc.identifier.issn | 1568-4946 | es |
dc.identifier.uri | https://hdl.handle.net/11441/133555 | |
dc.description.abstract | The problem of protein structure prediction (PSP) represents one of the most important challenges in
computational biology. Determining the three dimensional structure of proteins is necessary to under stand their functions at molecular level. The most representative soft computing approaches for solving
the protein tertiary structure prediction problem are summarized in this paper. These approaches have
been categorized following the type of methodology. A total of 90 relevant works published in last 15
years in the field of protein structure prediction have been reported, including the best competitors in
last CASP editions. However, despite large research effort in last decades, a considerable scope for further
improvement still remains in this area. | es |
dc.description.sponsorship | Junta de Andalucía P07-TIC-02611 | es |
dc.description.sponsorship | Ministerio de Educación y Ciencia TIN2011-28956-C02-01 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Applied Soft Computing, 35 (October 2015), 398-410. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Protein structure prediction | es |
dc.subject | Soft computing | es |
dc.subject | Protein contact map | es |
dc.subject | Support vector machines | es |
dc.subject | Neural networks | es |
dc.subject | Evolutionary algorithms | es |
dc.title | Soft computing methods for the prediction of protein tertiary structures: A survey | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | P07-TIC-02611 | es |
dc.relation.projectID | TIN2011-28956-C02-01 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1568494615003737?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.asoc.2015.06.024 | es |
dc.contributor.group | Universidad de Sevilla. TIC205: Ingeniería del Software Aplicada | es |
dc.journaltitle | Applied Soft Computing | es |
dc.publication.volumen | 35 | es |
dc.publication.issue | October 2015 | es |
dc.publication.initialPage | 398 | es |
dc.publication.endPage | 410 | es |
dc.identifier.sisius | 20877293 | es |
dc.contributor.funder | Junta de Andalucía | es |
dc.contributor.funder | Ministerio de Educación y Ciencia (MEC). España | es |