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Publicação
Leveraging Machine Learning For Predictive Modelling in Alzheimer´s Disease
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| 4.8 MB | Adobe PDF |
Orientador(es)
Resumo(s)
"Alzheimer’s disease (AD)’s complex aetheology results in a lack of effective therapies.
Studies have suggested the possible involvement of infectious agents and dysregulated
inflammation in its pathogenesis. Advances in Machine Learning (ML) permitted the
integration and analysis of high-dimensional heterogenous data, uncovering intricate
relationships and new disease biomarkers. In this dissertation ML models- including
linear and logistic regression, Decision Trees (DT), Random Forest (RF), Support Vector
Machine (SVM), K-Nearest Neighbors (KNN) and Extreme Gradient Boosting (XGB),
were deployed to predict AD, using Trained Immunity (TI), Infectious Burden (IB), and
serum cytokine data, as well as to model TI data itself, predicting levels of TNF𝛼, IL-6,
IL-10, IL-1𝛽 and IL-1RA in response to different infectious stimuli.(...)"
Descrição
Palavras-chave
Alzheimer’sdisease Trainedimmunity Cytokineprofile Machinelearning Inflammation Infectionhypothe- sis
Contexto Educativo
Citação
Editora
Instituto de Tecnologia Química e Biológica António Xavier. Universidade NOVA de Lisboa