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Activity Number: 59 - Deep Learning in Statistics: Really?!
Type: Topic Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #304674
Title: Incorporating Biological Network to Build Deep Learning Models for Gene Expression Data
Author(s): Tianwei Yu* and Yunchuan Kong
Companies: Emory University and Emory University
Keywords: neural networks; gene expression; RNAseq

Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared to the huge amount of features (p). This ``n< < p'' property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (1) there are tens of thousands of features and only hundreds of training samples, (2) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks. To address these issues and build a robust classification model, we propose methods to integrate external relational information and correlations of features into the deep neural network architecture. The methods are able to achieve sparse connection between network layers to prevent overfitting. We validated the methods on synthetic and real RNAseq data.

Authors who are presenting talks have a * after their name.

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