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GenNet framework: interpretable neural networks for phenotype prediction

By Arno van Hilten, Steven A Kushner, Manfred Kayser, M. Arfan Ikram, Hieab H.H. Adams, Caroline C.W. Klaver, Wiro J. Niessen, Gennady V. Roshchupkin

Posted 20 Jun 2020
bioRxiv DOI: 10.1101/2020.06.19.159152

Neural networks have been seldomly leveraged in population genomics due to the computational burden and challenge of interpretability. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotype from genotype. In this framework, public prior biological knowledge is used to construct interpretable and memory-efficient neural network architectures. These architectures obtain good predictive performance for multiple traits and complex diseases, opening the door for neural networks in population genomics. ### Competing Interest Statement Wiro J. Niessen is co-founder and shareholder of Quantib BV. Other authors declare no competing interests.

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