Modeling of proteins
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/29747
comunitat-uji-handle3:10234/162758
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TESISMetadades
Títol
Modeling of proteinsAutoria
Director/a
Castillo Solsona, RaquelPrograma de Doctorat
Programa de Doctorat en Química Teòrica i Modelització ComputacionalÒrgan responsable
Universitat Jaume I. Escola de DoctoratData de defensa
2023-11-10Descripció
Compendi d'articles
Editor
Universitat Jaume IParaules clau
Àrea de coneixement
Pàgines
198 p.Resum
In paper I the four proposed assumptions in the context of categorical variable mapping in protein classification problems: (1) translation, (2) permutation, (3) constant, and (4) eigenvalues were tested. The results ... [+]
In paper I the four proposed assumptions in the context of categorical variable mapping in protein classification problems: (1) translation, (2) permutation, (3) constant, and (4) eigenvalues were tested. The results suggest that these four assumptions are valid. In paper II the proposed approach is able to generate an accuracy, sensitivity and specify of classification forecasts of 97.69%, 95.02% and 98.26%, respectively, illustrating that a combination of DNA methylation with nonlinear methods such as artificial neural networks might be useful in the task of identifying patients with a carcinoma. In paper III it was shown that gene expression data can be successfully analyzed with machine learning techniques in order to differentiate healthy patients and patients with interstitial lung disease systemic sclerosis (ILD-SSc). In paper IV, following a machine learning approach, it was possible to identify a list of genes that appear to be related to inflammatory bowel disease [-]
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info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess