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A review of mechanistic learning in mathematical oncology
This review aims to capture the current state of the field and provide a perspective on how mechanistic learning may further progress in mathematical oncology.
Genetic prediction of quantitative traits: a machine learner's guide focused on height
Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding.
Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification
Owing to its pristine soft-tissue contrast and high resolution, structural magnetic resonance imaging (MRI) is widely applied in neurology, making it a valuable data source for image-based machine learning (ML) and deep learning applications.
Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design
Alzheimer's disease (AD) is associated with local (e. g. brain tissue atrophy) and global brain changes (loss of cerebral connectivity), which can be detected by high-resolution structural magnetic resonance imaging.
