{"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-learning-in-bioinformatics-introduction","title":"Deep learning in bioinformatics: introduction, application, and perspective in big data era","arxiv_id":"1903.00342","date":"2019-02-28","proceeding":null,"authors":["Yu Li","Chao Huang","Lizhong Ding","Zhongxiao Li","Yijie Pan","Xin Gao"],"abstract":"Deep learning, which is especially formidable in handling big data, has\nachieved great success in various fields, including bioinformatics. With the\nadvances of the big data era in biology, it is foreseeable that deep learning\nwill become increasingly important in the field and will be incorporated in\nvast majorities of analysis pipelines. In this review, we provide both the\nexoteric introduction of deep learning, and concrete examples and\nimplementations of its representative applications in bioinformatics. We start\nfrom the recent achievements of deep learning in the bioinformatics field,\npointing out the problems which are suitable to use deep learning. After that,\nwe introduce deep learning in an easy-to-understand fashion, from shallow\nneural networks to legendary convolutional neural networks, legendary recurrent\nneural networks, graph neural networks, generative adversarial networks,\nvariational autoencoder, and the most recent state-of-the-art architectures.\nAfter that, we provide eight examples, covering five bioinformatics research\ndirections and all the four kinds of data type, with the implementation written\nin Tensorflow and Keras. Finally, we discuss the common issues, such as\noverfitting and interpretability, that users will encounter when adopting deep\nlearning methods and provide corresponding suggestions. The implementations are\nfreely available at \\url{https://github.com/lykaust15/Deep_learning_examples}.","url_abs":"http://arxiv.org/abs/1903.00342v1","url_pdf":"http://arxiv.org/pdf/1903.00342v1.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-learning-in-bioinformatics-introduction","repo_url":"https://github.com/lykaust15/Deep_learning_examples","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}