Deep Learning Models to Accelerate Translational Cancer Genomics
Our ability to observe a large number of molecular measurements directly from patient tumors and germline is unprecedented. However, the ability to rationally infer meaning from these large datasets remains limited. Typical machine learning models can be trained to capture the complex relationship between the patient's profile (e.g. genomics and transcriptomics ) and different clinical outcomes (e.g. survival, progression, and drug response). While this is helpful in many cases, our ability to understand this relationship is still limited due to the “black box” nature of such models. Here, we will create a new model (P-net) that is a customized neural network with biologically-inspired architecture to both accurately predict biologically and clinically relevant outcomes and provide a better understanding of the interactions between different molecular components. The developed model will be used to predict the probability of cancer progression in prostate patients. Gaining a better understanding of the process of progression in cancer can guide clinical intervention, help identify potential molecular progression biomarkers, and enable new translational discoveries at the intersection of molecular profiling and clinical outcomes. The developed model will be available for researchers and practitioners to apply in different scenarios.
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Article: Nature.com Protein Misfolds – The protein tau is believed to stabilize the skeleton that shapes nerve cells, but in neurodegenerative diseases known as tauopathies, tau misfolds and stacks together to form filaments. In this week’s issue, Sjors Scheres, Michel Goedert, and their colleagues build on their previous work identifying different folds of tau filaments present in conditions such as Alzheimer’s disease. They reveal four additional folds relating to specific diseases...