التفاصيل البيبلوغرافية
| العنوان: |
Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data |
| المؤلفون: |
Fonseca, André, Spytek, Mikolaj, Biecek, Przemysław, Cordeiro, Clara, Sepúlveda, Nuno |
| المساهمون: |
Sapientia |
| بيانات النشر: |
BMC, 2024. |
| سنة النشر: |
2024 |
| مصطلحات موضوعية: |
Multivariate serological data, Super learner, Statistical modelling, Malaria outcome prediction, Random forest |
| الوصف: |
Nowadays, the chance of discovering the best antibody candidates for predicting clinical malaria has notably increased due to the availability of multi-sera data. The analysis of these data is typically divided into a feature selection phase followed by a predictive one where several models are constructed for predicting the outcome of interest. A key question in the analysis is to determine which antibodies should be included in the predictive stage and whether they should be included in the original or a transformed scale (i.e. binary/dichotomized). |
| الوصف (مترجم): |
Grant ref.: PPN/ULM/2020/1/00069/U/00001 |
| نوع الوثيقة: |
journal article |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| العلاقة: |
1756-0381 |
| DOI: |
10.1186/s13040-024-00354-4 |
| الإتاحة: |
http://hdl.handle.net/10400.1/20396Test |
| حقوق: |
open access |
| رقم الانضمام: |
rcaap.com.ualg.sapientia.ualg.pt.10400.1.20396 |
| قاعدة البيانات: |
RCAAP |
