[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125236-en":3,"doc-seo-125236-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125236,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Biomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning - Article","Pancreatic cancer is among the deadliest cancers and is often diagnosed at late stages. Biomarkers can enable more specific identification, yet many existing approaches rely on invasive sampling or established markers such as CA19-9. This study explores metabolite-based biomarkers using machine learning and enrichment with metabolomic workbench data and in silico detection and identification. From 106 control and cancer samples, 61 metabolites were analyzed and 8 were highlighted, with ethanol emerging as a strong candidate for pancreatic detection, while additional simulations are recommended for improved prognostic markers.","Indonesian Journal of Medical Chemistry and Bioinformatics  \nVolume 1  \nNumber 2 Biomarker Metabolite Discovery for Pancreatic Cancer using Ma-chine Learning  \nArticle 4  \n3-20-2023  \nBiomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning  \nImmanuelle Kezia  \nUniversitas Indonesia, [immanuelle.kezia@ui.ac.id](immanuelle.kezia@ui.ac.id)  \nLinda Erlina  \nUniversitas Indonesia, [linda.erlina22@ui.ac.id](linda.erlina22@ui.ac.id)  \naryo tedjo  \nUniversitas Indonesia, [1aryo.tedjo@gmail.com](1aryo.tedjo@gmail.com)  \nFadilah Fadilah  \nUniversitas Indonesia, [fadilah.msi@ui.ac.id](fadilah.msi@ui.ac.id)  \nFollow this and additional works at: [https://scholarhub.ui.ac.id/ijmcb](https://scholarhub.ui.ac.id/ijmcb)  \n Part of the Bioinformatics Commons, Cancer Biology Commons, and the Endocrine System Diseases Commons  \nRecommended Citation  \nKezia, Immanuelle; Erlina, Linda; tedjo, aryo; and Fadilah, Fadilah (2023) \"Biomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning,\" Indonesian Journal of Medical Chemistry and Bioinformatics: Vol. 1: No. 2, Article 4.  \nDOI: 10.7454/ijmcb.v1i2 .1017  \nAvailable at: [https://scholarhub.ui.ac.id/ijmcb/vol1/iss2/4](https://scholarhub.ui.ac.id/ijmcb/vol1/iss2/4)  \nThis Article is brought to you for free and open access by the Faculty of Medicine at UI Scholars Hub. It has been accepted for inclusion in Indonesian Journal of Medical Chemistry and Bioinformatics by an authorized editor of UI Scholars Hub.  \nBiomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning  \nAcknowledgements  \nWe would like to say thank you for the helping of Aryo Tedjo as a conceptor, Fadilah Fadilah as a supervisor, and Linda Erlina as a proofreader. Also we want to say thank you a lot for Xiamen University, Department of Electronic Science that has published their raw data of metabolite on the metabolomic workbench, so we can use it for further analysis.  \nThis article is available in Indonesian Journal of Medical Chemistry and Bioinformatics: [https://scholarhub.ui.ac.id/](https://scholarhub.ui.ac.id/)[ ](https://scholarhub.ui.ac.id/)ijmcb/vol1/iss2/4  \nArticle  \nBiomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning  \nImmanuelle Kezia 1*, Linda Erlina 1,2, Aryo Tedjo 1,2, Fadilah Fadilah 1,2,  \nCitation: Kezia, I.; Erlina, L.; Tedjo, A.; Fadilah, F. Biomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning . Ind. J. Med. Chem. Bio. IJMCB. 2023, 1, 2.  \nReceived: Wed Jan 25, 2023  \nAccepted: Mon Mar 13, 2023  \nPublished: Mon Mar 20, 2023  \nPublisher’s Note: IJMCB stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nCopyright: This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit [http://creativecommons.org/licenses/](http://creativecommons.org/licenses/)[ ](http://creativecommons.org/licenses/)[by/4.0/ or send a letter to Creative](by/4.0/ or send a letter to Creative)[ ](by/4.0/ or send a letter to Creative)Commons, PO Box 1866, Mountain View, CA 94042, USA.  \n1 Master’s Programme in Biomedical Sciences, Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Indonesia  \n2 Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Indonesia  \n* Correspondence: [immanuelle.kezia@ui.ac.id](immanuelle.kezia@ui.ac.id)  \nAbstract: Pancreatic cancer is one of the deadliest cancers in the world. This cancer is caused by multiple factors and mostly detected at late stadium. Biomarker is a marker that can identify some diseases very specific. For pancreatic cancer, biomarker has been recognized using blood sample known as liquid biopsy, breath, pancreatic secret, and tumor marker CA19-9 . Those biomarkers are invasive, so we want to identify the disease using a very convenient method. Metabolite is product from cell metabolism. Metabolites can become a biomarker especially from di","cbCaisPf9kr5r8DJ","https://ap.wps.com/l/cbCaisPf9kr5r8DJ","pdf",847017,1,12,"English","en",105,"# Abstract\n# Introduction\n## Disease background and current diagnosis\n# Acknowledgements\n# Article Details\n# Keywords","[{\"question\":\"What biomarker source does the study focus on for pancreatic cancer?\",\"answer\":\"The study focuses on metabolites as potential biomarkers derived from cell metabolism, aiming to support pancreatic cancer detection through metabolite signatures.\"},{\"question\":\"How were the metabolite data and identification performed?\",\"answer\":\"Metabolite data were obtained from the Metabolomic Workbench, and detection and identification were carried out in silico.\"},{\"question\":\"What were the key findings regarding important metabolites?\",\"answer\":\"From 106 samples, 61 metabolites were analyzed, and 8 metabolites were found to play important roles, with two identified as the most impactful, including ethanol as a strong candidate for detection.\"}]","Biomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning - 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