{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gennet-framework-interpretable-deep-learning","title":"GenNet framework: interpretable deep learning for predicting phenotypes from genetic data","arxiv_id":null,"date":"2021-09-17","proceeding":"Nature Communications Biology 2021 9","authors":["Arno van Hilten","Seven A. Kushner","Manfred Kayser","M. Arfan Ikram","Hieab H. H. Adams","Caroline C. W. Klaver","Wiro J. Niessen","Gennady V. Roshchupkin"],"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. \r\nWe 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. \r\nGenNet 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.","url_abs":"https://www.nature.com/articles/s42003-021-02622-z","url_pdf":"https://www.nature.com/articles/s42003-021-02622-z.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gennet-framework-interpretable-deep-learning","repo_url":"https://github.com/ArnovanHilten/GenNet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"efficient-neural-network","task_name":"Efficient Neural Network"},{"task_slug":"genetic-risk-prediction","task_name":"Genetic Risk Prediction"},{"task_slug":"medical-genetics","task_name":"Medical Genetics"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}