Publicação
A Study of Machine Learning for Artificial Intelligence-Based Enzyme Classification
| dc.contributor.author | Da Silva, Ana Patrícia Fernandes Brás | |
| dc.date.accessioned | 2025-02-25T15:05:05Z | |
| dc.date.available | 2025-02-25T15:05:05Z | |
| dc.date.issued | 2024-02-15 | |
| dc.description.abstract | "Enzyme Commission (EC) numbers prediction models allow for the prediction of an enzyme’s function by using only its sequence. They typically have 5 Levels with Level 0 to distinguish if the sequence is an enzyme and the Levels 1 to 4 analogous to each EC number digit. For the most part, these models use machine learning (ML) methods in order to predict the EC numbers, keeping the same structure across the four digits, despite the difference in data composition through the digits(...)." | pt_PT |
| dc.description.version | N/A | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/179775 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Universidade NOVA de Lisboa. Instituto de Tecnologia Química e Biológica António Xavier | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Genetic Programming | pt_PT |
| dc.subject | TPOT | pt_PT |
| dc.subject | EC number | pt_PT |
| dc.subject | Enzymes | pt_PT |
| dc.title | A Study of Machine Learning for Artificial Intelligence-Based Enzyme Classification | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Oeiras, Portugal | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |