dc.creator | Márquez Chamorro, Alfonso Eduardo | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.date.accessioned | 2022-05-24T06:15:36Z | |
dc.date.available | 2022-05-24T06:15:36Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Márquez Chamorro, A.E. y Aguilar Ruiz, J.S. (2015). Soft Computing Methods for Disulfide Connectivity Prediction. Evolutionary Bioinformatics, 11, 223-229. | |
dc.identifier.issn | 1176-9343 | es |
dc.identifier.uri | https://hdl.handle.net/11441/133553 | |
dc.description.abstract | The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can
be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists
in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the
cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative
soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such
as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are
used for the classification of these methods | 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 | 7 | es |
dc.language.iso | eng | es |
dc.publisher | Libertas Academica | es |
dc.relation.ispartof | Evolutionary Bioinformatics, 11, 223-229. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Disulfide connectivity prediction | es |
dc.subject | Protein structure prediction | es |
dc.subject | Soft computing | es |
dc.subject | Support vector machines | es |
dc.subject | Neural networks | es |
dc.title | Soft Computing Methods for Disulfide Connectivity Prediction | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | 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://journals.sagepub.com/doi/10.4137/EBO.S25349 | es |
dc.identifier.doi | 10.4137/EBO.S25349 | es |
dc.contributor.group | Universidad de Sevilla. TIC205: Ingeniería del Software Aplicada | es |
dc.journaltitle | Evolutionary Bioinformatics | es |
dc.publication.volumen | 11 | es |
dc.publication.initialPage | 223 | es |
dc.publication.endPage | 229 | es |
dc.identifier.sisius | 20877306 | es |
dc.contributor.funder | Junta de Andalucía | es |
dc.contributor.funder | Ministerio de Educación y Ciencia (MEC). España | es |