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DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

View ORCID ProfileMaxat Kulmanov, View ORCID ProfileRobert Hoehndorf
doi: https://doi.org/10.1101/839332
Maxat Kulmanov
1Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
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Robert Hoehndorf
1Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
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  • For correspondence: robert.hoehndorf@kaust.edu.sa
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Abstract

Motivation Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations.

Results We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from complete loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over state of the art methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene–disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno interact with a gene that is already associated with the predicted phenotype.

Availability https://github.com/bio-ontology-research-group/deeppheno

Contact robert.hoehndorf{at}kaust.edu.sa

Footnotes

  • Added hierarchical classification layer and gene expression value features to the prediction model.

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 4.0 International license.
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Posted March 25, 2020.
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DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier
Maxat Kulmanov, Robert Hoehndorf
bioRxiv 839332; doi: https://doi.org/10.1101/839332
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DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier
Maxat Kulmanov, Robert Hoehndorf
bioRxiv 839332; doi: https://doi.org/10.1101/839332

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