首页|Machine learning methods applied to classify complex diseases using genomic data
Machine learning methods applied to classify complex diseases using genomic data
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: "Complex diseases pose challenges in disease prediction due to their multifactor ial and polygenic nature. "In this work, we explored the prediction of two complex diseases, multiple scle rosis (MS) and Alzheimer\'s disease (AD), using machin e learning (ML) methods and genomic data from UK Biobank. Different ML methods w ere applied, including logistic regressions (LR), gradient boosting decision tre es (GB), extremely randomized trees (ET), random forest (RF), feedforward networ ks (FFN), and convolutional neural networks (CNN). The primary goal of this rese arch was to investigate the variability of ML models in classifying complex dise ases based on genomic risk. LR was the most robust method across folds and disea ses, whereas deep learning methods (FFN and CNN) exhibited high variability. Whe n comparing the performance of polygenic risk scores (PRS) with ML methods, PRS consistently performed at an average level.
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