Publication
Protein attributes-based predictive tool in a down syndrome mouse model: a machine learning approach
| dc.contributor.author | Ribeiro-Machado, Cláudia | |
| dc.contributor.author | Silva, Sara Costa | |
| dc.contributor.author | Aguiar, Sara | |
| dc.contributor.author | Faria, Brígida Mónica | |
| dc.date.accessioned | 2019-11-20T17:33:09Z | |
| dc.date.available | 2019-11-20T17:33:09Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Down syndrome is a disorder caused by an imbalance in the 21 chromosome, affecting learning and memorizing abilities, which was shown to be improved with memantine administration. In this study we intent to determine the most relevant proteins that could play a role in learning ability, suitable for possible biomarkers and to evaluate the accuracy of several bioinformatic models as a predictive tool. Five different supervised learning models (K-NN, DT, SVM, NB, NN) were applied to the original database and the newly created ones from eight attribute weighting models. Model accuracies were calculated through cross validation. Nine proteins revealed to be strong candidates as future biomarkers and K-NN and Neural Network had the better overall performances and highest accuracies (86.26% ± 0.23%; 81.51% ± 0.48%), which makes them a promising predictive tool to study protein profiles in DS patients’ follow-up after treatment with memantine. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Ribeiro-Machado, C., Silva, S. C., Aguiar, S., & Faria, B. M. (2018). Protein Attributes-Based Predictive Tool in a Down Syndrome Mouse Model: A Machine Learning Approach. Em Á. Rocha, H. Adeli, L. P. Reis, & S. Costanzo (Eds.), Trends and Advances in Information Systems and Technologies (pp. 19–28). Springer International Publishing. https://doi.org/10.1007/978-3-319-77700-9_3 | |
| dc.identifier.doi | 10.1007/978-3-319-77700-9_3 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/14876 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.relation.publisherversion | https://link.springer.com/chapter/10.1007%2F978-3-319-77700-9_3 | pt_PT |
| dc.subject | Down syndrome | pt_PT |
| dc.subject | Prediction | pt_PT |
| dc.subject | Learning improvement | pt_PT |
| dc.subject | Data mining | pt_PT |
| dc.subject | Attributes weighting | pt_PT |
| dc.subject | Classification | pt_PT |
| dc.title | Protein attributes-based predictive tool in a down syndrome mouse model: a machine learning approach | pt_PT |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 28 | pt_PT |
| oaire.citation.startPage | 19 | pt_PT |
| oaire.citation.title | Trends and Advances in Information Systems and Technologies. WorldCIST'18 | pt_PT |
| oaire.citation.volume | 747 | pt_PT |
| person.familyName | Aguiar | |
| person.familyName | Faria | |
| person.givenName | Sara | |
| person.givenName | Brigida Monica | |
| person.identifier | R-000-T1F | |
| person.identifier.ciencia-id | 0D1F-FB5E-55E4 | |
| person.identifier.orcid | 0000-0003-3642-5425 | |
| person.identifier.orcid | 0000-0003-2102-3407 | |
| person.identifier.rid | C-6649-2012 | |
| person.identifier.scopus-author-id | 6506476517 | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | bookPart | pt_PT |
| relation.isAuthorOfPublication | 1a5db31b-4b2c-423a-b493-d5a4ab0a890b | |
| relation.isAuthorOfPublication | 85832a40-7ef9-431a-be0c-78b45ebbae86 | |
| relation.isAuthorOfPublication.latestForDiscovery | 1a5db31b-4b2c-423a-b493-d5a4ab0a890b |