{"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/deep-transfer-learning-in-the-assessment-of","title":"Deep transfer learning in the assessment of the quality of protein models","arxiv_id":"1804.06281","date":"2018-04-17","proceeding":null,"authors":["David Menéndez Hurtado","Karolis Uziela","Arne Elofsson"],"abstract":"MOTIVATION: Proteins fold into complex structures that are crucial for their\nbiological functions. Experimental determination of protein structures is\ncostly and therefore limited to a small fraction of all known proteins. Hence,\ndifferent computational structure prediction methods are necessary for the\nmodelling of the vast majority of all proteins. In most structure prediction\npipelines, the last step is to select the best available model and to estimate\nits accuracy. This model quality estimation problem has been growing in\nimportance during the last decade, and progress is believed to be important for\nlarge scale modelling of proteins. The current generation of model quality\nestimation programs performs well at separating incorrect and good models, but\nfails to consistently identify the best possible model. State-of-the-art model\nquality assessment methods use a combination of features that describe a model\nand the agreement of the model with features predicted from the protein\nsequence.\n  RESULTS: We first introduce a deep neural network architecture to predict\nmodel quality using significantly fewer input features than state-of-the-art\nmethods. Thereafter, we propose a methodology to train the deep network that\nleverages the comparative structure of the problem. We also show the\npossibility of applying transfer learning on databases of known protein\nstructures. We demonstrate its viability by reaching state-of-the-art\nperformance using only a reduced set of input features and a coarse description\nof the models.\n  AVAILABILITY: The code will be freely available for download at\ngithub.com/ElofssonLab/ProQ4.","url_abs":"http://arxiv.org/abs/1804.06281v1","url_pdf":"http://arxiv.org/pdf/1804.06281v1.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":"deep-transfer-learning-in-the-assessment-of","repo_url":"https://github.com/ElofssonLab/ProQ4","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.06281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06281"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ElofssonLab/ProQ4","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"0bbb95c78ba6ad91","entry":"convert","repo":"ElofssonLab/ProQ4","repo_kind":"official","path":"proq4/casp-run.py","file_url":"https://github.com/ElofssonLab/ProQ4/blob/HEAD/proq4/casp-run.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"0bbb95c78ba6ad91"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}