{"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/genet-deep-representations-for-metagenomics","title":"GeNet: Deep Representations for Metagenomics","arxiv_id":"1901.11015","date":"2019-01-30","proceeding":null,"authors":["Mateo Rojas-Carulla","Ilya Tolstikhin","Guillermo Luque","Nicholas Youngblut","Ruth Ley","Bernhard Schölkopf"],"abstract":"We introduce GeNet, a method for shotgun metagenomic classification from raw\nDNA sequences that exploits the known hierarchical structure between labels for\ntraining. We provide a comparison with state-of-the-art methods Kraken and\nCentrifuge on datasets obtained from several sequencing technologies, in which\ndataset shift occurs. We show that GeNet obtains competitive precision and good\nrecall, with orders of magnitude less memory requirements. Moreover, we show\nthat a linear model trained on top of representations learned by GeNet achieves\nrecall comparable to state-of-the-art methods on the aforementioned datasets,\nand achieves over 90% accuracy in a challenging pathogen detection problem.\nThis provides evidence of the usefulness of the representations learned by\nGeNet for downstream biological tasks.","url_abs":"http://arxiv.org/abs/1901.11015v1","url_pdf":"http://arxiv.org/pdf/1901.11015v1.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":"genet-deep-representations-for-metagenomics","repo_url":"https://github.com/mrojascarulla/GeNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"genet-deep-representations-for-metagenomics","repo_url":"https://github.com/JainSamyak8840/metagenomics_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"genet-deep-representations-for-metagenomics","repo_url":"https://github.com/2023-MindSpore-1/ms-code-214/tree/main/GENet_Res50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"genet-deep-representations-for-metagenomics","repo_url":"https://github.com/2023-MindSpore-4/Code10/tree/main/GENet_Res50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"genet-deep-representations-for-metagenomics","repo_url":"https://github.com/MindSpore-paper-code-3/code7/tree/main/GENet_Res50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.11015","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}