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GTC ON-DEMAND

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Abstract:
We'll describe our work to develop multi-task deep learning models for the improved genetic risk prediction of coronary artery disease. Although studies have shown that basic coronary artery disease genetic-risk prediction models provide modest clinical utility, improved comprehensive models can make this a reality of clinical practice. This information can help guide therapy decisions and provide an impetus for optimizing lifestyle modifications, thereby improving health outcomes and clinical efficiency. Some preliminary models have been described for autoencoding genetic data, but these models pay no attention to the underlying structure of genetic data. We'll talk about our work to provide best practices for autoencoding genetic data, with the ultimate goal of using these latent genetic factors as input for improved neural network and deep learning-based genetic risk prediction models.
We'll describe our work to develop multi-task deep learning models for the improved genetic risk prediction of coronary artery disease. Although studies have shown that basic coronary artery disease genetic-risk prediction models provide modest clinical utility, improved comprehensive models can make this a reality of clinical practice. This information can help guide therapy decisions and provide an impetus for optimizing lifestyle modifications, thereby improving health outcomes and clinical efficiency. Some preliminary models have been described for autoencoding genetic data, but these models pay no attention to the underlying structure of genetic data. We'll talk about our work to provide best practices for autoencoding genetic data, with the ultimate goal of using these latent genetic factors as input for improved neural network and deep learning-based genetic risk prediction models.  Back
 
Topics:
Genomics & Bioinformatics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9663
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