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For: Sallis BF, Erkert L, Moñino-Romero S, Acar U, Wu R, Konnikova L, Lexmond WS, Hamilton MJ, Dunn WA, Szepfalusi Z, Vanderhoof JA, Snapper SB, Turner JR, Goldsmith JD, Spencer LA, Nurko S, Fiebiger E. An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning. J Allergy Clin Immunol 2018;141:1354-1364.e9. [PMID: 29273402 DOI: 10.1016/j.jaci.2017.11.027] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 1.8] [Reference Citation Analysis]
Number Citing Articles
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2 Nuutinen M, Haukka J, Virkkula P, Torkki P, Toppila-Salmi S. Using machine learning for the personalised prediction of revision endoscopic sinus surgery. PLoS One 2022;17:e0267146. [PMID: 35486626 DOI: 10.1371/journal.pone.0267146] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
3 Visaggi P, Barberio B, Gregori D, Azzolina D, Martinato M, Hassan C, Sharma P, Savarino E, de Bortoli N. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther 2022. [PMID: 35098562 DOI: 10.1111/apt.16778] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
4 Wechsler JB, Ackerman SJ, Chehade M, Amsden K, Riffle ME, Wang MY, Du J, Kleinjan ML, Alumkal P, Gray E, Kim KA, Wershil BK, Kagalwalla AF. Noninvasive biomarkers identify eosinophilic esophagitis: A prospective longitudinal study in children. Allergy 2021;76:3755-65. [PMID: 33905577 DOI: 10.1111/all.14874] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
5 Maag E, Kulasingam A, Grove EL, Pedersen KS, Kristensen SD, Hvas AM. Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction. Sci Rep 2021;11:13787. [PMID: 34215806 DOI: 10.1038/s41598-021-93162-3] [Reference Citation Analysis]
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7 Rostam Niakan Kalhori S, Tanhapour M, Gholamzadeh M. Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine. J Biomed Inform 2021;115:103687. [PMID: 33497811 DOI: 10.1016/j.jbi.2021.103687] [Reference Citation Analysis]
8 Ferrante G, Licari A, Fasola S, Marseglia GL, La Grutta S, Peters R. Artificial intelligence in the diagnosis of pediatric allergic diseases. Pediatr Allergy Immunol 2021;32:405-13. [DOI: 10.1111/pai.13419] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
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10 Sallis BF, Acar U, Hawthorne K, Babcock SJ, Kanagaratham C, Goldsmith JD, Rosen R, Vanderhoof JA, Nurko S, Fiebiger E. A Distinct Esophageal mRNA Pattern Identifies Eosinophilic Esophagitis Patients With Food Impactions. Front Immunol 2018;9:2059. [DOI: 10.3389/fimmu.2018.02059] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 0.8] [Reference Citation Analysis]
11 Obeso D, Mera-Berriatua L, Rodríguez-Coira J, Rosace D, Fernández P, Martín-Antoniano IA, Santaolalla M, Marco Martín G, Chivato T, Fernández-Rivas M, Ramos T, Blanco C, Alvarado MI, Domínguez C, Angulo S, Barbas C, Barber D, Villaseñor A, Escribese MM. Multi-omics analysis points to altered platelet functions in severe food-associated respiratory allergy. Allergy 2018;73:2137-49. [PMID: 30028518 DOI: 10.1111/all.13563] [Cited by in Crossref: 45] [Cited by in F6Publishing: 42] [Article Influence: 11.3] [Reference Citation Analysis]