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White-box Deep Neural Network Prediction of Genome-Wide Transcriptome Signatures

View ORCID ProfileRasmus Magnusson, Jesper N. Tegnér, Mika Gustafsson
doi: https://doi.org/10.1101/2021.02.11.430730
Rasmus Magnusson
1Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
2School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden
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  • For correspondence: rasmus.magnusson@liu.se
Jesper N. Tegnér
3Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955–6900, Saudi Arabia
4Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
5Science for Life Laboratory, Solna, Sweden
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Mika Gustafsson
1Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
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Abstract

Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). To this end, we explore whether a neural network (NN) could predict the transcriptome from TFs. Using at least one hidden layer, we find that the expression of 1,600 TFs can explain >95% of variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an overrepresentation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the target genes’ dysregulation (rho=0.61, P < 10−216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. In conclusion, we demonstrate the construction of an interpretable neural network predictor. Analysis of the predictors revealed key TFs that were inducing transcriptional changes during disease.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted February 12, 2021.
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White-box Deep Neural Network Prediction of Genome-Wide Transcriptome Signatures
Rasmus Magnusson, Jesper N. Tegnér, Mika Gustafsson
bioRxiv 2021.02.11.430730; doi: https://doi.org/10.1101/2021.02.11.430730
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White-box Deep Neural Network Prediction of Genome-Wide Transcriptome Signatures
Rasmus Magnusson, Jesper N. Tegnér, Mika Gustafsson
bioRxiv 2021.02.11.430730; doi: https://doi.org/10.1101/2021.02.11.430730

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