{"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/protein-secondary-structure-prediction-using","title":"Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks","arxiv_id":"1604.07176","date":"2016-04-25","proceeding":null,"authors":["Zhen Li","Yizhou Yu"],"abstract":"Protein secondary structure prediction is an important problem in\nbioinformatics. Inspired by the recent successes of deep neural networks, in\nthis paper, we propose an end-to-end deep network that predicts protein\nsecondary structures from integrated local and global contextual features. Our\ndeep architecture leverages convolutional neural networks with different kernel\nsizes to extract multiscale local contextual features. In addition, considering\nlong-range dependencies existing in amino acid sequences, we set up a\nbidirectional neural network consisting of gated recurrent unit to capture\nglobal contextual features. Furthermore, multi-task learning is utilized to\npredict secondary structure labels and amino-acid solvent accessibility\nsimultaneously. Our proposed deep network demonstrates its effectiveness by\nachieving state-of-the-art performance, i.e., 69.7% Q8 accuracy on the public\nbenchmark CB513, 76.9% Q8 accuracy on CASP10 and 73.1% Q8 accuracy on CASP11.\nOur model and results are publicly available.","url_abs":"http://arxiv.org/abs/1604.07176v1","url_pdf":"http://arxiv.org/pdf/1604.07176v1.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":"protein-secondary-structure-prediction-using","repo_url":"https://github.com/icemansina/IJCAI2016","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"protein-secondary-structure-prediction","task_name":"Protein Secondary Structure Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.07176","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}