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GenNet framework: interpretable deep learning for predicting phenotypes from genetic data
- Source :
- Communications Biology, Vol 4, Iss 1, Pp 1-9 (2021), Communications Biology, 4, Communications Biology, 4(1), Communications Biology, 4, 1, Communications Biology, 4(1):1094. Springer Nature, Communications Biology
- Publication Year :
- 2021
- Publisher :
- Nature Portfolio, 2021.
-
Abstract
- Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found well-replicated genes such as HERC2 and OCA2 for hair and eye color, and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.<br />van Hilten and colleagues present GenNet, a deep-learning framework for predicting phenotype from genetic data. This framework generates interpretable neural networks that provide insight into the genetic basis of complex traits and diseases.
- Subjects :
- Population genetics
Epidemiology
Computer science
QH301-705.5
Schizophrenia (object-oriented programming)
Medicine (miscellaneous)
Computational biology
Article
Sensory disorders Donders Center for Medical Neuroscience [Radboudumc 12]
General Biochemistry, Genetics and Molecular Biology
Population genomics
Novel gene
Deep Learning
SDG 3 - Good Health and Well-being
Machine learning
Humans
Biology (General)
Artificial neural network
business.industry
Deep learning
Genetic data
Phenotype
Genetic architecture
Neural Networks, Computer
Artificial intelligence
General Agricultural and Biological Sciences
business
Software
Subjects
Details
- Language :
- English
- ISSN :
- 23993642
- Volume :
- 4
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Communications Biology
- Accession number :
- edsair.doi.dedup.....6bd94ac64c141e2a1af50cb4161eebc8