{"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-supervised-and-convolutional-generative","title":"Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction","arxiv_id":"1403.1347","date":"2014-03-06","proceeding":null,"authors":["Jian Zhou","Olga G. Troyanskaya"],"abstract":"Predicting protein secondary structure is a fundamental problem in protein\nstructure prediction. Here we present a new supervised generative stochastic\nnetwork (GSN) based method to predict local secondary structure with deep\nhierarchical representations. GSN is a recently proposed deep learning\ntechnique (Bengio & Thibodeau-Laufer, 2013) to globally train deep generative\nmodel. We present the supervised extension of GSN, which learns a Markov chain\nto sample from a conditional distribution, and applied it to protein structure\nprediction. To scale the model to full-sized, high-dimensional data, like\nprotein sequences with hundreds of amino acids, we introduce a convolutional\narchitecture, which allows efficient learning across multiple layers of\nhierarchical representations. Our architecture uniquely focuses on predicting\nstructured low-level labels informed with both low and high-level\nrepresentations learned by the model. In our application this corresponds to\nlabeling the secondary structure state of each amino-acid residue. We trained\nand tested the model on separate sets of non-homologous proteins sharing less\nthan 30% sequence identity. Our model achieves 66.4% Q8 accuracy on the CB513\ndataset, better than the previously reported best performance 64.9% (Wang et\nal., 2011) for this challenging secondary structure prediction problem.","url_abs":"http://arxiv.org/abs/1403.1347v1","url_pdf":"http://arxiv.org/pdf/1403.1347v1.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-supervised-and-convolutional-generative","repo_url":"https://github.com/LucaAngioloni/ProteinSecondaryStructure-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"protein-secondary-structure-prediction","task_name":"Protein Secondary Structure Prediction"},{"task_slug":"protein-structure-prediction","task_name":"Protein Structure Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1403.1347","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}