{"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/toward-the-automated-analysis-of-complex","title":"Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming","arxiv_id":"1702.01780","date":"2017-02-06","proceeding":null,"authors":["Andrew Sohn","Randal S. Olson","Jason H. Moore"],"abstract":"Machine learning has been gaining traction in recent years to meet the demand\nfor tools that can efficiently analyze and make sense of the ever-growing\ndatabases of biomedical data in health care systems around the world. However,\neffectively using machine learning methods requires considerable domain\nexpertise, which can be a barrier of entry for bioinformaticians new to\ncomputational data science methods. Therefore, off-the-shelf tools that make\nmachine learning more accessible can prove invaluable for bioinformaticians. To\nthis end, we have developed an open source pipeline optimization tool\n(TPOT-MDR) that uses genetic programming to automatically design machine\nlearning pipelines for bioinformatics studies. In TPOT-MDR, we implement\nMultifactor Dimensionality Reduction (MDR) as a feature construction method for\nmodeling higher-order feature interactions, and combine it with a new expert\nknowledge-guided feature selector for large biomedical data sets. We\ndemonstrate TPOT-MDR's capabilities using a combination of simulated and real\nworld data sets from human genetics and find that TPOT-MDR significantly\noutperforms modern machine learning methods such as logistic regression and\neXtreme Gradient Boosting (XGBoost). We further analyze the best pipeline\ndiscovered by TPOT-MDR for a real world problem and highlight TPOT-MDR's\nability to produce a high-accuracy solution that is also easily interpretable.","url_abs":"http://arxiv.org/abs/1702.01780v1","url_pdf":"http://arxiv.org/pdf/1702.01780v1.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":"toward-the-automated-analysis-of-complex","repo_url":"https://github.com/Jocker1980/ML_2019_GeneticAlgorithm_AutoMachineLearning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"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}