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On Transformative Adaptive Activation Functions in Neural Networks for Gene Expression Inference

View ORCID ProfileVladimír Kunc, View ORCID ProfileJiří Kléma
doi: https://doi.org/10.1101/587287
Vladimír Kunc
Department of Computer Science, Czech Technical University in Prague, Prague 121 35, Czech Republic, kuncvlad@fel.cvut.cz
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  • For correspondence: kuncvlad@fel.cvut.cz
Jiří Kléma
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Abstract

Motivation Gene expression profiling was made cheaper by the NIH LINCS program that profiles only ~1, 000 selected landmark genes and uses them to reconstruct the whole profile. The D–GEX method employs neural networks to infer the whole profile. However, the original D–GEX can be further significantly improved.

Results We have analyzed the D–GEX method and determined that the inference can be improved using a logistic sigmoid activation function instead of the hyperbolic tangent. Moreover, we propose a novel transformative adaptive activation function that improves the gene expression inference even further and which generalizes several existing adaptive activation functions. Our improved neural network achieves average mean absolute error of 0.1340 which is a significant improvement over our reimplementation of the original D–GEX which achieves average mean absolute error 0.1637

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 24, 2019.
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On Transformative Adaptive Activation Functions in Neural Networks for Gene Expression Inference
Vladimír Kunc, Jiří Kléma
bioRxiv 587287; doi: https://doi.org/10.1101/587287
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On Transformative Adaptive Activation Functions in Neural Networks for Gene Expression Inference
Vladimír Kunc, Jiří Kléma
bioRxiv 587287; doi: https://doi.org/10.1101/587287

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