{"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/a-pathway-based-kernel-boosting-method-for","title":"A pathway-based kernel boosting method for sample classification using genomic data","arxiv_id":"1803.03910","date":"2018-03-11","proceeding":null,"authors":["Li Zeng","Zhaolong Yu","Hongyu Zhao"],"abstract":"The analysis of cancer genomic data has long suffered \"the curse of\ndimensionality\". Sample sizes for most cancer genomic studies are a few\nhundreds at most while there are tens of thousands of genomic features studied.\nVarious methods have been proposed to leverage prior biological knowledge, such\nas pathways, to more effectively analyze cancer genomic data. Most of the\nmethods focus on testing marginal significance of the associations between\npathways and clinical phenotypes. They can identify relevant pathways, but do\nnot involve predictive modeling. In this article, we propose a Pathway-based\nKernel Boosting (PKB) method for integrating gene pathway information for\nsample classification, where we use kernel functions calculated from each\npathway as base learners and learn the weights through iterative optimization\nof the classification loss function. We apply PKB and several competing methods\nto three cancer studies with pathological and clinical information, including\ntumor grade, stage, tumor sites, and metastasis status. Our results show that\nPKB outperforms other methods, and identifies pathways relevant to the outcome\nvariables.","url_abs":"http://arxiv.org/abs/1803.03910v1","url_pdf":"http://arxiv.org/pdf/1803.03910v1.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":"a-pathway-based-kernel-boosting-method-for","repo_url":"https://github.com/zengliX/PKB","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}