[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123755-en":3,"doc-seo-123755-105":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},123755,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data","High-accuracy prediction enables better selection between FOLFOX and FOLFIRI, two standard first-line chemotherapy options for colorectal cancer, yet treatment choice has not been fully clarified. A newly developed machine learning model is trained on public gene expression datasets from GEO to derive molecular signatures predictive of the efficacy of 5-FU based combination therapy. The approach uses 5-fold cross-validation with LASSO and VarSelRF feature selection and evaluates classifiers including random forest and support vector machines. Validation and test results exceed 90% accuracy with 85–95% specificity and sensitivity.","TYPE Original Research PUBLISHED 18 January 2024  \nDOI 10.3389/fphys.2023.1272206  \nOPEN ACCESS  \nEDITED BY  \nRajesh kumar Tripathy,  \nBirla Institute of Technology and Science, India  \nREVIEWED BY  \nJennie L. Williams,  \nStony Brook University, United States Elife Zerrin Bagci,  \nNamik Kemal University, Türkiye  \n*CORRESPONDENCE  \nMohsin Saleet Jafri,  [sjafri@gmu.edu](sjafri@gmu.edu)  \nRECEIVED 03 August 2023  \nACCEPTED 26 December 2023  \nPUBLISHED 18 January 2024  \nCITATION  \nAmniouel S and Jafri MS (2024), High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data.  \nFront. Physiol. 14:1272206 .  \ndoi: 10.3389/fphys.2023.1272206  \nCOPYRIGHT  \n© 2024 Amniouel and Jafri. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nHigh-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data  \nSoukaina Amniouel 1 and Mohsin Saleet Jafri 1,2*  \n1School of Systems Biology, George Mason University, Fairfax, VA, United States, 2Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, MD, United States  \nIntroduction: FOLFOX and FOLFIRI chemotherapy are considered standard firstline treatment options for colorectal cancer (CRC) . However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed.  \nMethods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models.  \nResults and Discussion: For the CRC GEO dataset samples from patients who received either FOLFOX or FOLFIRI, validation and test sets were >90% correctly classified (accuracy), with specificity and sensitivity ranging between 85%-95% . In the datasets used from the GEO database, 28.6% of patients who failed the treatment therapy they received are predicted to benefit from the alternative treatment. Analysis of the gene signature suggests the mechanistic difference between colorectal cancers that respond and those that do not respond to FOLFOX and FOLFIRI. Application of this machine learning approach could lead to improvements in treatment outcomes for patients with CRC and other cancers after additional appropriate clinical validation.  \nKEYWORDS  \ncolorectal cancer, FOLFOX, FOLFIRI, chemoresistance, machine learning, gene expression, feature selection  \n1 Introduction  \nColorectal cancer (CRC) is the most frequent malignant disease of the gastrointestinal tract, the third most frequent cancer affecting both men and women and is oneofthe leading causes of cancer-related morbidity and mortality in spite of widespread, effective measures of preventive screening, and major advances in treatment options (Fouad et al., 2018; Sung et al., 2021). In recent decades, the overall long-term outcome of patients curatively resected has not significantly changed. The 5-year survival rate for CRC is 63% but drops to 14% for metastatic CRC. More than half of colorectal adenocarcinomas are still diagnosed only when the disease involves regional or distant structures (Araghi et al., 2021). Thus, further investigation is still needed to develop effective approaches for medical in","cbCaif4w5hDHtcMI","https://ap.wps.com/l/cbCaif4w5hDHtcMI","pdf",3967482,1,23,"English","en",105,"# Introduction\n# Methods\n# Results and Discussion\n# Keywords","[{\"question\":\"What problem does the study address in colorectal cancer treatment selection?\",\"answer\":\"The study targets the lack of thoroughly analyzed criteria for choosing between FOLFOX and FOLFIRI for colorectal cancer patients.\"},{\"question\":\"How is the machine learning model built and evaluated?\",\"answer\":\"The model is trained on GEO gene expression data using 5-fold cross-validation, LASSO and VarSelRF feature selection, and classifiers such as random forest and support vector machines.\"},{\"question\":\"What performance results are reported for predicting chemotherapy efficacy?\",\"answer\":\"Validation and test sets correctly classify samples at greater than 90% accuracy, with specificity and sensitivity ranging from 85% to 95%.\"}]","High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data | 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problem does the study address in colorectal cancer treatment selection?","Question",{"text":75,"@type":76},"The study targets the lack of thoroughly analyzed criteria for choosing between FOLFOX and FOLFIRI for colorectal cancer patients.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the machine learning model built and evaluated?",{"text":80,"@type":76},"The model is trained on GEO gene expression data using 5-fold cross-validation, LASSO and VarSelRF feature selection, and classifiers such as random forest and support vector machines.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results are reported for predicting chemotherapy efficacy?",{"text":84,"@type":76},"Validation and test sets correctly classify samples at greater than 90% accuracy, with specificity and sensitivity ranging from 85% to 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