Deep Learning Methods for Predicting Disease Status Using Genomic Data
Oct 1, 2018·,
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0 min read
Qiong Wu
Alounso Boueiz
Alican Bozkurt
Afsaneh Masoomi
Ao Wang
Dawn L. DeMeo
Scott T. Weiss
Fei Wang
Dana H. Brooks
Jennifer G. Dy
Abstract
Deep learning has achieved remarkable success in image recognition and natural language processing, but its application to genomic data for disease status prediction remains underexplored. In this work, we evaluate several deep learning architectures for predicting disease status from high-dimensional genomic data, including genome-wide association study (GWAS) data. We compare different approaches and demonstrate that deep learning methods can outperform traditional machine learning methods on genomic classification tasks.
Type
Publication
In Journal of Biometrics & Biostatistics 9(5), 417 (2018)
Authors
AI Scientist
I am an AI Scientist at Paige AI. I did my Ph.D. with Jennifer Dy, Dana Brooks, and Jan-Willem van de Meent at Northeastern University. My main research interests are machine learning with emphasis on probabilistic programming, deep neural networks, and their applications in biomedical image processing. I am one of the developers of Probabilistic Torch, a library for deep generative models that extends PyTorch. I am also one of the maintainers of the PyTorch distributions module.