Machine learning methods applied to classify complex diseases using genomic data
Magdalena Arnal Segura,
Giorgio Bini,
Anastasia Krithara
et al.
Abstract:Complex diseases pose challenges in disease prediction due to their multifactorial and polygenic nature. In this work, we explored the prediction of two complex diseases, multiple sclerosis (MS) and Alzheimer's disease (AD), using machine learning (ML) methods and genomic data from UK Biobank. Different ML methods were applied, including logistic regressions (LR), gradient boosting decision trees (GB), extremely randomized trees (ET), random forest (RF), feedforward networks (FFN), and convolutional neural net… Show more
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