Imputation methods for serologic biomarkers in inflammatory bowel disease

Dataemia
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Summarize this content to 100 words: Lucas López, R., Grande Burgos, M. J., Gálvez, A. & Pérez Pulido, R. The human gastrointestinal tract and oral microbiota in inflammatory bowel disease: a state of the science review. APMIS 125 (1), 3–10 (2017).
Google Scholar 
Kuna, A. T. Serological markers of inflammatory bowel disease. Vol. 23, Biochemia Medica. (2013).Sura, S. P., Ahmed, A., Cheifetz, A. S. & Moss, A. C. Characteristics of inflammatory bowel disease serology in patients with indeterminate colitis. J. Clin. Gastroenterol. ;48(4). (2014).Lee, W. I., Subramaniam, K., Hawkins, C. A. & Randall, K. L. The significance of ANCA positivity in patients with inflammatory bowel disease. Pathology ;51(6). (2019).Prideaux, L., De Cruz, P., Ng, S. C. & Kamm, M. A. Serological Antibodies in Inflammatory Bowel Disease: A Systematic Review. Inflamm. Bowel Dis. 18 (7), 1340–1355 (2012).
Google Scholar 
Van Schaik, F. D. M. et al. Serological markers predict inflammatory bowel disease years before the diagnosis. Gut 62(5). (2013).Li, X., Conklin, L. & Alex, P. New serological biomarkers of inflammatory bowel disease Vol. 14 World J. Gastroenterol., (2008).Austin, P. C., White, I. R., Lee, D. S. & van Buuren, S. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Can. J. Cardiol. 37 (9), 1322–1331 (2021).
Google Scholar 
Wisniewski, S. R., Leon, A. C., Otto, M. W. & Trivedi, M. H. Prevention of Missing Data in Clinical Research Studies. Vol. 59, Biol. Psychiatr.. (2006).Khan, S. I. & Hoque, A. S. M. L. SICE: an improved missing data imputation technique. J. Big Data. 7 (1), 37 (2020).
Google Scholar 
Sun, Y., Li, J., Xu, Y., Zhang, T. & Wang, X. Deep learning versus conventional methods for missing data imputation: A review and comparative study. Expert Syst. Appl. 227, 120201 (2023).
Google Scholar 
Tang, F. & Ishwaran, H. Random forest missing data algorithms. Stat. Anal. Data Min. ;10(6). (2017).Verpoort, P. C., MacDonald, P. & Conduit, G. J. Materials data validation and imputation with an artificial neural network. Comput. Mater. Sci. ;147. (2018).Choudhury, S. J. & Pal, N. R. Imputation of missing data with neural networks for classification. Knowl. Based Syst. 182. (2019).Lin, W. C., Tsai, C. F. & Zhong, J. R. Deep learning for missing value imputation of continuous data and the effect of data discretization. Knowl. Based Syst. ;239. (2022).Gómez-Carracedo, M. P., Andrade, J. M., López-Mahía, P., Muniategui, S. & Prada, D. A practical comparison of single and multiple imputation methods to handle complex missing data in air quality datasets. Chemometr. Intell. Lab. Syst. 134, 23–33 (2014).
Google Scholar 
Sullivan, T. R., White, I. R., Salter, A. B., Ryan, P. & Lee, K. J. Should multiple imputation be the method of choice for handling missing data in randomized trials? Stat. Methods Med. Res. 27(9). (2018).Zhang, Z. Missing data imputation: Focusing on single imputation. Ann. Transl Med. 4(1). (2016).Rubin, D. B. Inference and missing data. Biometrika 63(3). (1976).Pang, Y. et al. Assessment of clinical activity and severity using serum ANCA and ASCA antibodies in patients with ulcerative colitis. Allergy Asthma Clin. Immunol. ;16(1). (2020).Morgan, N. N. et al. Crohn’s Disease Patients Uniquely Contain Inflammatory Responses to Flagellin in a CD4 Effector Memory Subset. Inflamm. Bowel Dis. ;28(12). (2022).Shome, M. et al. Serological profiling of Crohn’s disease and ulcerative colitis patients reveals anti-microbial antibody signatures. World J. Gastroenterol. 28(30). (2022).Faisal, S. & Tutz, G. Multiple imputation using nearest neighbor methods. Inf. Sci. (N Y). 570, 500–516 (2021).
Google Scholar 
Blazek, K., van Zwieten, A., Saglimbene, V. & Teixeira-Pinto, A. A practical guide to multiple imputation of missing data in nephrology. Kidney Inter. Vol. 99 (2021).Jadhav, A., Pramod, D. & Ramanathan, K. Comparison of Performance of Data Imputation Methods for Numeric Dataset. Appl. Artif. Intell. 33(10):913–933. (2019).van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45(3). (2011).Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12. (2011).Paszke, A. et al. PyTorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems. (2019).Lin, W. C. & Tsai, C. F. Missing value imputation: a review and analysis of the literature (2006–2017). Artif. Intell. Rev. ;53(2). (2020).Niass, O., Diongue, A. K. & Touré, A. Analysis of missing data in sero-epidemiological studies. Afr. J. Appl. Stat. 2 (1), 29–37 (2015).
Google Scholar 



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