Amino-Acid Profiling and Machine Learning in Crimean--Congo Hemorrhagic Fever Disease Progression

Amino-Acid Profiling and Machine Learning in Crimean--Congo Hemorrhagic Fever Disease Progression | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 20 January 2026 V1 Latest version Share on Amino-Acid Profiling and Machine Learning in Crimean--Congo Hemorrhagic Fever Disease Progression Authors : Ahu Cephe , Necla Koçhan 0000-0003-2355-4826 , Seyit Büyüktuna , Gözde Ertürk Zararsız , Serra İlayda Yerlitaş , Kübra Doğan 0000-0002-9448-3407 , Selda Özer , Cihad Baysal , Yasemin Çakır 0000-0001-5510-3216 , Caner Öksüz , Halef Doğan 0000-0001-8738-0760 , and Gökmen Zararsız [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176892856.66996472/v1 154 views 54 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study aimed to evaluate the potential of amino-acid profiles to predict disease progression in patients with Crimean–Congo Hemorrhagic Fever (CCHF) and to identify metabolic biomarkers associated with clinical outcomes and survival. Of the 115 confirmed CCHF patients, 18 required intensive care unit (ICU) admission and 16 died. Notably, 15 of the deaths occurred among ICU patients, whereas only one death occurred outside the ICU. For each patient, 32 amino acid concentrations were used as input for machine-learning (ML) models. Among the classification models evaluated for predicting ICU admission, XGBOOST and LASSO achieved the highest performance, each with an AUC of 0.958. Arginine and glutamic acid consistently emerged as the most influential features across all models, followed by 1-methyl-L-histidine, tryptophan, and tyrosine, which appeared among the top variables in four of the five best-performing models. In survival models, the top five amino acids contributing to predictions were ornithine, gamma-aminobutyric acid, ethanolamine, arginine, and histidine. ML models based on amino-acid profiles can accurately predict disease progression in CCHF, supporting early risk stratification and providing insights into the metabolic mechanisms underlying disease severity. Supplementary Material File (manuscript.docx) Download 668.74 KB Information & Authors Information Version history V1 Version 1 20 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords biostatistics & bioinformatics crimean-congo hemorrhagic fever virus survival analysis virus classification Authors Affiliations Ahu Cephe Erciyes Universitesi View all articles by this author Necla Koçhan 0000-0003-2355-4826 Izmir Ekonomi Universitesi View all articles by this author Seyit Büyüktuna Cumhuriyet Universitesi Tip Fakultesi View all articles by this author Gözde Ertürk Zararsız Erciyes Universitesi View all articles by this author Serra İlayda Yerlitaş Erciyes Universitesi View all articles by this author Kübra Doğan 0000-0002-9448-3407 TC Saglik Bakanligi Sivas Numune Hastanesi View all articles by this author Selda Özer Sivas Cumhuriyet Universitesi View all articles by this author Cihad Baysal Cumhuriyet Universitesi Tip Fakultesi View all articles by this author Yasemin Çakır 0000-0001-5510-3216 Cumhuriyet Universitesi Tip Fakultesi View all articles by this author Caner Öksüz Cumhuriyet Universitesi Tip Fakultesi View all articles by this author Halef Doğan 0000-0001-8738-0760 Sivas Cumhuriyet Universitesi View all articles by this author Gökmen Zararsız [email protected] Erciyes Universitesi View all articles by this author Metrics & Citations Metrics Article Usage 154 views 54 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ahu Cephe, Necla Koçhan, Seyit Büyüktuna, et al. Amino-Acid Profiling and Machine Learning in Crimean--Congo Hemorrhagic Fever Disease Progression. Authorea . 20 January 2026. DOI: https://doi.org/10.22541/au.176892856.66996472/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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