{"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/contextual-regression-an-accurate-and","title":"Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific Data","arxiv_id":"1710.10728","date":"2017-10-30","proceeding":null,"authors":["Chengyu Liu","Wei Wang"],"abstract":"Machine learning algorithms such as linear regression, SVM and neural network\nhave played an increasingly important role in the process of scientific\ndiscovery. However, none of them is both interpretable and accurate on\nnonlinear datasets. Here we present contextual regression, a method that joins\nthese two desirable properties together using a hybrid architecture of neural\nnetwork embedding and dot product layer. We demonstrate its high prediction\naccuracy and sensitivity through the task of predictive feature selection on a\nsimulated dataset and the application of predicting open chromatin sites in the\nhuman genome. On the simulated data, our method achieved high fidelity recovery\nof feature contributions under random noise levels up to 200%. On the open\nchromatin dataset, the application of our method not only outperformed the\nstate of the art method in terms of accuracy, but also unveiled two previously\nunfound open chromatin related histone marks. Our method can fill the blank of\naccurate and interpretable nonlinear modeling in scientific data mining tasks.","url_abs":"http://arxiv.org/abs/1710.10728v1","url_pdf":"http://arxiv.org/pdf/1710.10728v1.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":"contextual-regression-an-accurate-and","repo_url":"https://github.com/HomoSapienLCY/Contextual_Regression","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"network-embedding","task_name":"Network Embedding"},{"task_slug":"feature-selection","task_name":"feature selection"},{"task_slug":"regression-1","task_name":"regression"},{"task_slug":"scientific-discovery","task_name":"scientific discovery"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}