{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T04:02:47Z","timestamp":1779163367690,"version":"3.51.4"},"posted":{"date-parts":[[2024,6,28]]},"group-title":"Bioinformatics","reference-count":114,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,2,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Myalgic Encephalomyelitis\/Chronic Fatigue Syndrome (ME\/CFS) is a chronic illness with a multifactorial etiology and heterogeneous symptomatology, posing major challenges for diagnosis and treatment. Here, we present BioMapAI, a supervised deep neural network trained on a four-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data, and detailed clinical symptoms. By simultaneously modeling these diverse data types to predict clinical severity, BioMapAI identifies disease- and symptom-specific biomarkers and robustly classifies ME\/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct the first connectivity map spanning the microbiome, immune system, and plasma metabolome in health and ME\/CFS, adjusted for age, gender, and additional clinical factors. This map uncovers disrupted associations between microbial metabolism (e.g., short-chain fatty acids, branched-chain amino acids, tryptophan, benzoate), plasma lipids and bile acids, and heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, \u03b3\u03b4T) secreting IFN\u03b3 and GzA. Overall, BioMapAI provides unprecedented systems-level insights into ME\/CFS, refining existing hypotheses and hypothesizing new pathways associated to the disease\u2019s heterogeneous symptoms.<\/jats:p>","DOI":"10.1101\/2024.06.24.600378","type":"posted-content","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T20:15:18Z","timestamp":1719605718000},"source":"Crossref","is-referenced-by-count":6,"title":["BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis \/ Chronic Fatigue Syndrome"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8107-3452","authenticated-orcid":false,"given":"Ruoyun","family":"Xiong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elizabeth","family":"Aiken","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2348-6352","authenticated-orcid":false,"given":"Ryan","family":"Caldwell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5241-8914","authenticated-orcid":false,"given":"Suzanne D.","family":"Vernon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Kozhaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5203-7409","authenticated-orcid":false,"given":"Courtney","family":"Gunter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9546-9922","authenticated-orcid":false,"given":"Lucinda","family":"Bateman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8898-6633","authenticated-orcid":false,"given":"Derya","family":"Unutmaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5534-7023","authenticated-orcid":false,"given":"Julia","family":"Oh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025021622250615000_2024.06.24.600378v2.1","doi-asserted-by":"crossref","unstructured":"Cortes Rivera, M. , Mastronardi, C. , Silva-Aldana, C. T. , Arcos-Burgos, M. C , Lidbury, B. A. Myalgic Encephalomyelitis\/Chronic Fatigue Syndrome: A Comprehensive Review. Diagnostics G, 91 (2019).","DOI":"10.3390\/diagnostics9030091"},{"key":"2025021622250615000_2024.06.24.600378v2.2","doi-asserted-by":"crossref","unstructured":"Sweetman, E. et al. Current Research Provides Insight into the Biological Basis and Diagnostic Potential for Myalgic Encephalomyelitis\/Chronic Fatigue Syndrome (ME\/CFS). Diagnostics G, 73 (2019).","DOI":"10.3390\/diagnostics9030073"},{"key":"2025021622250615000_2024.06.24.600378v2.3","first-page":"e113629","article-title":"A Comprehensive Update of the Current Understanding of Chronic Fatigue Syndrome. Anesthesiol","volume":"11","year":"2021","journal-title":"Pain Med"},{"key":"2025021622250615000_2024.06.24.600378v2.4","doi-asserted-by":"crossref","unstructured":"Ruiz-Pablos, M. , Paiva, B. , Montero-Mateo, R. , Garcia, N. C , Zabaleta, A. Epstein-Barr Virus and the Origin of Myalgic Encephalomyelitis or Chronic Fatigue Syndrome. Front. Immunol. 12, 656797 (2021).","DOI":"10.3389\/fimmu.2021.656797"},{"key":"2025021622250615000_2024.06.24.600378v2.5","doi-asserted-by":"publisher","DOI":"10.1128\/mbio.02540-20"},{"key":"2025021622250615000_2024.06.24.600378v2.6","doi-asserted-by":"crossref","unstructured":"Anderson, D. E. et al. Lack of cross-neutralization by SARS patient sera towards SARS-CoV-2. Emerg. Microbes Infect. G, 900\u2013902 (2020).","DOI":"10.1080\/22221751.2020.1761267"},{"key":"2025021622250615000_2024.06.24.600378v2.7","doi-asserted-by":"publisher","DOI":"10.1093\/occmed\/kqi013"},{"key":"2025021622250615000_2024.06.24.600378v2.8","first-page":"232546","article-title":"Implications of Heterogeneity in Multiple Myeloma","volume":"2014","year":"2014","journal-title":"BioMed Res. Int"},{"key":"2025021622250615000_2024.06.24.600378v2.9","unstructured":"Wallstrom, G. , Anderson, K. S. C , LaBaer, J. Biomarker Discovery for Heterogeneous Diseases."},{"key":"2025021622250615000_2024.06.24.600378v2.10","doi-asserted-by":"crossref","unstructured":"Weyand, C. M. , McCarthy, T. G. C , Goronzy, J. J. Correlation between disease phenotype and genetic heterogeneity in rheumatoid arthritis. J. Clin. Invest. G5, 2120\u20132126 (1995).","DOI":"10.1172\/JCI117900"},{"key":"2025021622250615000_2024.06.24.600378v2.11","doi-asserted-by":"crossref","unstructured":"Poenaru, S. , Abdallah, S. J. , Corrales-Medina, V. C , Cowan, J. COVID-19 and post-infectious myalgic encephalomyelitis\/chronic fatigue syndrome: a narrative review. Ther. Adv. Infect. Dis. 8, 20499361211009385 (2021).","DOI":"10.1177\/20499361211009385"},{"key":"2025021622250615000_2024.06.24.600378v2.12","doi-asserted-by":"publisher","DOI":"10.1007\/s00406-023-01661-3"},{"key":"2025021622250615000_2024.06.24.600378v2.13","doi-asserted-by":"crossref","unstructured":"Hare, P. J. , LaGree, T. J. , Byrd, B. A. , DeMarco, A. M. C , Mok, W. W. K. Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters. Microorganisms G, 2277 (2021).","DOI":"10.3390\/microorganisms9112277"},{"key":"2025021622250615000_2024.06.24.600378v2.14","doi-asserted-by":"crossref","unstructured":"Cohen, R. M. , Haggerty, S. C , Herman, W. H. HbA1c for the Diagnosis of Diabetes and Prediabetes: Is It Time for a Mid-Course Correction? J. Clin. Endocrinol. Metab. G5, 5203\u2013 5206 (2010).","DOI":"10.1210\/jc.2010-2352"},{"key":"2025021622250615000_2024.06.24.600378v2.15","doi-asserted-by":"crossref","unstructured":"Zhou, W. et al. Longitudinal multi-omics of host\u2013microbe dynamics in prediabetes. Nature 56G, 663\u2013671 (2019).","DOI":"10.1038\/s41586-019-1236-x"},{"key":"2025021622250615000_2024.06.24.600378v2.16","doi-asserted-by":"publisher","DOI":"10.4143\/crt.2020.206"},{"key":"2025021622250615000_2024.06.24.600378v2.17","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz038"},{"key":"2025021622250615000_2024.06.24.600378v2.18","doi-asserted-by":"crossref","unstructured":"Bretherick, A. D. et al. Typing myalgic encephalomyelitis by infection at onset: A DecodeME study. NIHR Open Res. 3, 20 (2023).","DOI":"10.3310\/nihropenres.13421.4"},{"key":"2025021622250615000_2024.06.24.600378v2.19","doi-asserted-by":"crossref","unstructured":"Bae, J. C , Lin, J.-M. S. Healthcare Utilization in Myalgic Encephalomyelitis\/Chronic Fatigue Syndrome (ME\/CFS): Analysis of US Ambulatory Healthcare Data, 2000\u20132009. Front. Pediatr. 7, (2019).","DOI":"10.3389\/fped.2019.00185"},{"key":"2025021622250615000_2024.06.24.600378v2.20","doi-asserted-by":"crossref","unstructured":"Zheng, Y. C , Zhu, Z. Editorial: Retrieving meaningful patterns from big biomedical data with machine learning approaches. Front. Genet. 14, (2023).","DOI":"10.3389\/fgene.2023.1177996"},{"key":"2025021622250615000_2024.06.24.600378v2.21","doi-asserted-by":"crossref","unstructured":"Leelatian, N. et al. Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells. eLife G, e56879 (2020).","DOI":"10.7554\/eLife.56879.sa2"},{"key":"2025021622250615000_2024.06.24.600378v2.22","doi-asserted-by":"publisher","DOI":"10.1016\/j.chom.2024.04.005"},{"key":"2025021622250615000_2024.06.24.600378v2.23","doi-asserted-by":"crossref","first-page":"e00579","DOI":"10.14309\/ctg.0000000000000579","article-title":"Clinical Value of Multiomics-Based Biomarker Signatures in Inflammatory Bowel Diseases: Challenges and Opportunities","volume":"14","year":"2023","journal-title":"Clin. Transl. Gastroenterol"},{"key":"2025021622250615000_2024.06.24.600378v2.24","unstructured":"Marcos-Zambrano, L. J. et al. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front. Microbiol. 12, (2021)."},{"key":"2025021622250615000_2024.06.24.600378v2.25","doi-asserted-by":"publisher","DOI":"10.1016\/j.chom.2023.01.004"},{"key":"2025021622250615000_2024.06.24.600378v2.26","doi-asserted-by":"crossref","unstructured":"Raijmakers, R. P. H. et al. Multi-omics examination of \u01ea fever fatigue syndrome identifies similarities with chronic fatigue syndrome. J. Transl. Med. 18, 448 (2020).","DOI":"10.1186\/s12967-020-02585-5"},{"key":"2025021622250615000_2024.06.24.600378v2.27","doi-asserted-by":"crossref","unstructured":"Germain, A. , et al. Plasma metabolomics reveals disrupted response and recovery following maximal exercise in myalgic encephalomyelitis\/chronic fatigue syndrome. JCI Insight 7, (2023).","DOI":"10.1172\/jci.insight.157621"},{"key":"2025021622250615000_2024.06.24.600378v2.28","doi-asserted-by":"publisher","DOI":"10.3390\/ijms23147906"},{"key":"2025021622250615000_2024.06.24.600378v2.29","doi-asserted-by":"crossref","unstructured":"Li\u00f1ares-Blanco, J. , Fernandez-Lozano, C. , Seoane, J. A. C , L\u00f3pez-Campos, G. Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes. Front. Microbiol. 13, (2022).","DOI":"10.3389\/fmicb.2022.872671"},{"key":"2025021622250615000_2024.06.24.600378v2.30","doi-asserted-by":"publisher","DOI":"10.2196\/41065"},{"key":"2025021622250615000_2024.06.24.600378v2.31","doi-asserted-by":"publisher","DOI":"10.12688\/gatesopenres.13131.2"},{"key":"2025021622250615000_2024.06.24.600378v2.32","doi-asserted-by":"crossref","unstructured":"Mora-Ortiz, M. , Trichard, M. , Oregioni, A. C , Claus, S. P. Thanatometabolomics: introducing NMR-based metabolomics to identify metabolic biomarkers of the time of death. Metabolomics 15, 37 (2019).","DOI":"10.1007\/s11306-019-1498-1"},{"key":"2025021622250615000_2024.06.24.600378v2.33","doi-asserted-by":"crossref","unstructured":"Balasubramanian, R. et al. Metabolomic profiles associated with all-cause mortality in the Women\u2019s Health Initiative. Int. J. Epidemiol. 4G, 289\u2013300 (2020).","DOI":"10.1093\/ije\/dyz211"},{"key":"2025021622250615000_2024.06.24.600378v2.34","doi-asserted-by":"publisher","DOI":"10.3390\/nu14122539"},{"key":"2025021622250615000_2024.06.24.600378v2.35","doi-asserted-by":"publisher","DOI":"10.1111\/ggi.14684"},{"key":"2025021622250615000_2024.06.24.600378v2.36","doi-asserted-by":"crossref","unstructured":"Peng, S. , Shen, Y. , Wang, M. C , Zhang, J. Serum and CSF Metabolites in Stroke-Free Patients Are Associated With Vascular Risk Factors and Cognitive Performance. Front. Aging Neurosci. 12, (2020).","DOI":"10.3389\/fnagi.2020.00193"},{"key":"2025021622250615000_2024.06.24.600378v2.37","doi-asserted-by":"publisher","DOI":"10.1111\/jnc.15607"},{"key":"2025021622250615000_2024.06.24.600378v2.38","doi-asserted-by":"crossref","unstructured":"Pomrenze, M. B. , Paliarin, F. C , Maiya, R. Friend of the Devil: Negative Social Influences Driving Substance Use Disorders. Front. Behav. Neurosci. 16, (2022).","DOI":"10.3389\/fnbeh.2022.836996"},{"key":"2025021622250615000_2024.06.24.600378v2.39","doi-asserted-by":"publisher","DOI":"10.1111\/add.16049"},{"key":"2025021622250615000_2024.06.24.600378v2.40","doi-asserted-by":"crossref","unstructured":"Carco, C. et al. Increasing Evidence That Irritable Bowel Syndrome and Functional Gastrointestinal Disorders Have a Microbial Pathogenesis. Front. Cell. Infect. Microbiol. 10, (2020).","DOI":"10.3389\/fcimb.2020.00468"},{"key":"2025021622250615000_2024.06.24.600378v2.41","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-09964-7"},{"key":"2025021622250615000_2024.06.24.600378v2.42","doi-asserted-by":"crossref","unstructured":"Liang, S. , Wu, X. , Hu, X. , Wang, T. C , Jin, F. Recognizing Depression from the Microbiota\u2013Gut\u2013Brain Axis. Int. J. Mol. Sci. 1G, 1592 (2018).","DOI":"10.3390\/ijms19061592"},{"key":"2025021622250615000_2024.06.24.600378v2.43","doi-asserted-by":"publisher","DOI":"10.3390\/nu14102081"},{"key":"2025021622250615000_2024.06.24.600378v2.44","doi-asserted-by":"publisher","DOI":"10.1007\/s11938-023-00433-1"},{"key":"2025021622250615000_2024.06.24.600378v2.45","doi-asserted-by":"publisher","DOI":"10.1007\/s00125-023-06032-0"},{"key":"2025021622250615000_2024.06.24.600378v2.46","doi-asserted-by":"publisher","DOI":"10.1017\/S0007114520000380"},{"key":"2025021622250615000_2024.06.24.600378v2.47","doi-asserted-by":"publisher","DOI":"10.1002\/dta.2891"},{"key":"2025021622250615000_2024.06.24.600378v2.48","doi-asserted-by":"crossref","unstructured":"Wang, J.-H. et al. Clinical evidence of the link between gut microbiome and myalgic encephalomyelitis\/chronic fatigue syndrome: a retrospective review. Eur. J. Med. Res. 2G, 148 (2024).","DOI":"10.1186\/s40001-024-01747-1"},{"key":"2025021622250615000_2024.06.24.600378v2.49","doi-asserted-by":"crossref","unstructured":"Lenoir, M. et al. Butyrate mediates anti-inflammatory effects of Faecalibacterium prausnitzii in intestinal epithelial cells through Dact3. Gut Microbes (2020).","DOI":"10.21203\/rs.3.rs-28864\/v1"},{"key":"2025021622250615000_2024.06.24.600378v2.50","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0804812105"},{"key":"2025021622250615000_2024.06.24.600378v2.51","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2014-307649"},{"key":"2025021622250615000_2024.06.24.600378v2.52","doi-asserted-by":"publisher","DOI":"10.1128\/mbio.00300-15"},{"key":"2025021622250615000_2024.06.24.600378v2.53","first-page":"e00889","article-title":"Revealing the Bacterial Butyrate Synthesis Pathways by Analyzing (Meta)genomic Data","volume":"5","year":"2021","journal-title":"mBio"},{"key":"2025021622250615000_2024.06.24.600378v2.54","doi-asserted-by":"publisher","DOI":"10.3390\/nu15102275"},{"key":"2025021622250615000_2024.06.24.600378v2.55","doi-asserted-by":"publisher","DOI":"10.3390\/nu11020448"},{"key":"2025021622250615000_2024.06.24.600378v2.56","first-page":"e00814","article-title":"Engineering Escherichia coli for Glutarate Production as the C5 Platform Backbone","volume":"84","year":"2018","journal-title":"Appl. Environ. Microbiol"},{"key":"2025021622250615000_2024.06.24.600378v2.57","doi-asserted-by":"publisher","DOI":"10.1096\/fj.202002284RR"},{"key":"2025021622250615000_2024.06.24.600378v2.58","doi-asserted-by":"publisher","DOI":"10.4103\/0975-7406.168029"},{"key":"2025021622250615000_2024.06.24.600378v2.59","doi-asserted-by":"crossref","unstructured":"Kim, C.-S . Roles of Diet-Associated Gut Microbial Metabolites on Brain Health: Cell-to-Cell Interactions between Gut Bacteria and the Central Nervous System. Adv. Nutr. 15, 100136 (2024).","DOI":"10.1016\/j.advnut.2023.10.008"},{"key":"2025021622250615000_2024.06.24.600378v2.60","doi-asserted-by":"publisher","DOI":"10.3390\/ijms231710128"},{"key":"2025021622250615000_2024.06.24.600378v2.61","doi-asserted-by":"publisher","DOI":"10.1002\/alz.056142"},{"key":"2025021622250615000_2024.06.24.600378v2.62","doi-asserted-by":"publisher","DOI":"10.1186\/s40169-019-0243-8"},{"key":"2025021622250615000_2024.06.24.600378v2.63","unstructured":"Ahmed, Z. , Zeeshan, S. , Xiong, R. C , Liang, B. T. PAS-Gen: Guide to iOS app with gene-disease."},{"key":"2025021622250615000_2024.06.24.600378v2.64","doi-asserted-by":"crossref","unstructured":"Ahmed, Z. , Wan, S. , Zhang, F. C , Zhong, W. Artificial intelligence for omics data analysis. BMC Methods 1, 4 (2024).","DOI":"10.1186\/s44330-024-00004-5"},{"key":"2025021622250615000_2024.06.24.600378v2.65","first-page":"baaa010","article-title":"Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine","volume":"2020","year":"2020","journal-title":"Database J. Biol. Databases Curation"},{"key":"2025021622250615000_2024.06.24.600378v2.66","doi-asserted-by":"publisher","DOI":"10.1016\/j.chom.2023.01.001"},{"key":"2025021622250615000_2024.06.24.600378v2.67","doi-asserted-by":"crossref","unstructured":"Germain, A. , Ruppert, D. , Levine, S. M. C , Hanson, M. R. Prospective Biomarkers from Plasma Metabolomics of Myalgic Encephalomyelitis\/Chronic Fatigue Syndrome Implicate Redox Imbalance in Disease Symptomatology. Metabolites 8, 90 (2018).","DOI":"10.3390\/metabo8040090"},{"key":"2025021622250615000_2024.06.24.600378v2.68","doi-asserted-by":"crossref","unstructured":"Lim, E.-J. C , Son, C.-G. Review of case definitions for myalgic encephalomyelitis\/chronic fatigue syndrome (ME\/CFS). J. Transl. Med. 18, 289 (2020).","DOI":"10.1186\/s12967-020-02455-0"},{"key":"2025021622250615000_2024.06.24.600378v2.69","doi-asserted-by":"crossref","unstructured":"Germain, A. , Barupal, D. K. , Levine, S. M. C , Hanson, M. R. Comprehensive Circulatory Metabolomics in ME\/CFS Reveals Disrupted Metabolism of Acyl Lipids and Steroids. Metabolites 10, 34 (2020).","DOI":"10.3390\/metabo10010034"},{"key":"2025021622250615000_2024.06.24.600378v2.70","doi-asserted-by":"publisher","DOI":"10.1177\/17423953211043106"},{"key":"2025021622250615000_2024.06.24.600378v2.71","doi-asserted-by":"crossref","unstructured":"Hanson, M. R . The viral origin of myalgic encephalomyelitis\/chronic fatigue syndrome. PLOS Pathog. 1G, e1011523 (2023).","DOI":"10.1371\/journal.ppat.1011523"},{"key":"2025021622250615000_2024.06.24.600378v2.72","first-page":"e3951","article-title":"Impact of mHealth Chronic Disease Management on Treatment Adherence and Patient Outcomes: A Systematic Review","volume":"17","year":"2015","journal-title":"J. Med. Internet Res"},{"key":"2025021622250615000_2024.06.24.600378v2.73","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.publhealth.24.100901.141021"},{"key":"2025021622250615000_2024.06.24.600378v2.74","doi-asserted-by":"crossref","unstructured":"Derman, I. D. et al. High-throughput bioprinting of the nasal epithelium using patient-derived nasal epithelial cells. Biofabrication 15, 044103 (2023).","DOI":"10.1088\/1758-5090\/aced23"},{"key":"2025021622250615000_2024.06.24.600378v2.75","doi-asserted-by":"crossref","unstructured":"Fleming, E. et al. Cultivation of common bacterial species and strains from human skin, oral, and gut microbiota. BMC Microbiol. 21, 278 (2021).","DOI":"10.1186\/s12866-021-02314-y"},{"key":"2025021622250615000_2024.06.24.600378v2.76","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-058128"},{"key":"2025021622250615000_2024.06.24.600378v2.77","doi-asserted-by":"publisher","DOI":"10.1101\/2023.02.10.23285630"},{"key":"2025021622250615000_2024.06.24.600378v2.78","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0201066"},{"key":"2025021622250615000_2024.06.24.600378v2.79","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0212193"},{"key":"2025021622250615000_2024.06.24.600378v2.80","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-62157-x"},{"key":"2025021622250615000_2024.06.24.600378v2.81","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinthera.2019.02.012"},{"key":"2025021622250615000_2024.06.24.600378v2.82","doi-asserted-by":"crossref","unstructured":"Das, S. , Taylor, K. , Kozubek, J. , Sardell, J. C , Gardner, S. Genetic risk factors for ME\/CFS identified using combinatorial analysis. J. Transl. Med. 20, 598 (2022).","DOI":"10.1186\/s12967-022-03815-8"},{"key":"2025021622250615000_2024.06.24.600378v2.83","doi-asserted-by":"publisher","DOI":"10.3978\/j.issn.2072-1439.2015.10.63"},{"key":"2025021622250615000_2024.06.24.600378v2.84","doi-asserted-by":"publisher","DOI":"10.1007\/s11165-004-3437-y"},{"key":"2025021622250615000_2024.06.24.600378v2.85","doi-asserted-by":"publisher","DOI":"10.2478\/arsm-2021-0029"},{"key":"2025021622250615000_2024.06.24.600378v2.86","first-page":"58","article-title":"Role of training data variability on classifier performance and generalizability","volume":"10581","year":"2018","journal-title":"in Medical Imaging 2018: Digital Pathology"},{"key":"2025021622250615000_2024.06.24.600378v2.87","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207329"},{"key":"2025021622250615000_2024.06.24.600378v2.88","doi-asserted-by":"crossref","unstructured":"Krumina, A. et al. Clinical Profile and Aspects of Differential Diagnosis in Patients with ME\/CFS from Latvia. Medicina (Mex .) 57, 958 (2021).","DOI":"10.3390\/medicina57090958"},{"key":"2025021622250615000_2024.06.24.600378v2.89","doi-asserted-by":"crossref","unstructured":"Zubcevik, N. et al. Symptom Clusters and Functional Impairment in Individuals Treated for Lyme Borreliosis. Front. Med. 7, (2020).","DOI":"10.3389\/fmed.2020.00464"},{"key":"2025021622250615000_2024.06.24.600378v2.90","first-page":"300","article-title":"Pericardite l\u00fapica: dor tor\u00e1cica e febre em tempos de COVID-19","volume":"38","year":"2022","journal-title":"Rev. Port. Med. Geral E Fam"},{"key":"2025021622250615000_2024.06.24.600378v2.91","doi-asserted-by":"publisher","DOI":"10.7205\/MILMED-D-12-00438"},{"key":"2025021622250615000_2024.06.24.600378v2.92","doi-asserted-by":"crossref","unstructured":"Resnick, H. E. C , Lathan, C. E. From battlefield to home: a mobile platform for assessing brain health. mHealth 2, 30 (2016).","DOI":"10.21037\/mhealth.2016.07.02"},{"key":"2025021622250615000_2024.06.24.600378v2.93","doi-asserted-by":"crossref","unstructured":"Lee, J. et al. Hemodynamics during the 10-minute NASA Lean Test: evidence of circulatory decompensation in a subset of ME\/CFS patients. J. Transl. Med. 18, 314 (2020).","DOI":"10.1186\/s12967-020-02481-y"},{"key":"2025021622250615000_2024.06.24.600378v2.94","unstructured":"Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis\/ChronicFatigue Syndrome, Board on the Health of Select Populations, C Institute of Medicine. Beyond Myalgic Encephalomyelitis\/Chronic Fatigue Syndrome: Redefining an Illness. (National Academies Press (US), Washington (DC), 2015)."},{"key":"2025021622250615000_2024.06.24.600378v2.95","unstructured":"Monica, 1776 Main Street Santa C California 90401-3208. 36-Item Short Form Survey (SF-36) Scoring Instructions. https:\/\/www.rand.org\/health-care\/surveys_tools\/mos\/36-item-short-form\/scoring.html."},{"key":"2025021622250615000_2024.06.24.600378v2.96","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0163962"},{"key":"2025021622250615000_2024.06.24.600378v2.97","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-023-01688-w"},{"key":"2025021622250615000_2024.06.24.600378v2.98","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq461"},{"key":"2025021622250615000_2024.06.24.600378v2.99","doi-asserted-by":"crossref","unstructured":"Love, M. I. , Huber, W. C , Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).","DOI":"10.1186\/s13059-014-0550-8"},{"key":"2025021622250615000_2024.06.24.600378v2.100","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009442"},{"key":"2025021622250615000_2024.06.24.600378v2.101","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"2025021622250615000_2024.06.24.600378v2.102","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"2025021622250615000_2024.06.24.600378v2.103","doi-asserted-by":"publisher","DOI":"10.1145\/3490725.3490748"},{"key":"2025021622250615000_2024.06.24.600378v2.104","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1705.07874"},{"key":"2025021622250615000_2024.06.24.600378v2.105","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1605.08695"},{"key":"2025021622250615000_2024.06.24.600378v2.106","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1201.0490"},{"key":"2025021622250615000_2024.06.24.600378v2.107","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"},{"key":"2025021622250615000_2024.06.24.600378v2.108","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v028.i05"},{"key":"2025021622250615000_2024.06.24.600378v2.109","doi-asserted-by":"crossref","unstructured":"Langfelder, P. C , Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics G, 559 (2008).","DOI":"10.1186\/1471-2105-9-559"},{"key":"2025021622250615000_2024.06.24.600378v2.110","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2311.10260"},{"key":"2025021622250615000_2024.06.24.600378v2.111","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcm.2016.02.012"},{"key":"2025021622250615000_2024.06.24.600378v2.112","doi-asserted-by":"publisher","DOI":"10.2307\/2344614"},{"key":"2025021622250615000_2024.06.24.600378v2.113","doi-asserted-by":"crossref","unstructured":"Bakdash, J. Z. C , Marusich, L. R. Repeated Measures Correlation. Front. Psychol. 8, (2017).","DOI":"10.3389\/fpsyg.2017.00456"},{"key":"2025021622250615000_2024.06.24.600378v2.114","unstructured":"Gu, C. Smoothing Spline ANOVA Models: R Package gss. Smoothing Spline ANOVA Models."}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2024.06.24.600378","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T06:37:50Z","timestamp":1768459070000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2024.06.24.600378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":114,"URL":"https:\/\/doi.org\/10.1101\/2024.06.24.600378","relation":{},"subject":[],"published":{"date-parts":[[2024,6,28]]},"subtype":"preprint"}}