Abstract
Molecular diagnosis is based on the quantification of RNA, proteins, or metabolites whose concentration can be correlated to clinical situations. Usually, these molecules are not suitable for early diagnosis or to follow clinical evolution. Large-scale diagnosis using these types of molecules depends on cheap and preferably noninvasive strategies for screening. Saliva has been studied as a noninvasive, easily obtainable diagnosis fluid, and the presence of serum proteins in it enhances its use as a systemic health status monitoring tool. With a recently described automated capillary electrophoresis-based strategy that allows us to obtain a salivary total protein profile, it is possible to quantify and analyze patterns that may indicate disease presence or absence. The data of 19 persons with diabetes and 58 healthy donors obtained by capillary electrophoresis were transformed, treated, and grouped so that the structured values could be used to study individuals’ health state. After Pairwise Relationships and Hierarchical Clustering analysis were observed that amplitudes of protein peaks present in the saliva of these individuals could be used as differentiating parameters between healthy and unhealthy people. It indicates that these characteristics can serve as input for a future computational intelligence algorithm that will aid in the stratification of individuals that manifest changes in salivary proteins.
Supported by Universidade de Pernambuco (UPE) and Universidade Católica Portuguesa (UCP).
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Arrais, J.P., et al.: Oralcard: a bioinformatic tool for the study of oral proteome. Arch. Oral Biol. 58(7), 762–772 (2013)
Castagnola, M., et al.: Salivary biomarkers and proteomics: future diagnostic and clinical utilities. Acta Otorhinolaryngol. Ital. 37(2), 94 (2017)
Conde, J., de la Fuente, J.M., Baptista, P.V.: RNA quantification using gold nanoprobes-application to cancer diagnostics. J. Nanobiotechnol. 8(1), 5 (2010)
Cruz, I., et al.: SalivaPRINT toolkit-protein profile evaluation and phenotype stratification. J. Proteomics 171, 81–86 (2018)
David, H.A.: The Method of Paired Comparisons, vol. 12, London (1963)
Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)
Esteves., E., Cruz., I., Esteves., A.C., Barros., M., Rosa., N.: SalivaPRINT as a non-invasive diagnostic tool. In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, HEALTHINF, vol. 5, pp. 677–682. INSTICC. SciTePress (2020). https://doi.org/10.5220/0009163506770682
Ferreira, A.V., Bastos Filho, C.J., Lins, A.J.: An unsupervised analysis of an Alzheimer’s disease patient population using subspace search and hierarchical density-based clustering. In: 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1–6. IEEE (2019)
Frigui, H., Krishnapuram, R.: Clustering by competitive agglomeration. Pattern Recogn. 30(7), 1109–1119 (1997)
Kaczor-Urbanowicz, K.E., Martin Carreras-Presas, C., Aro, K., Tu, M., Garcia-Godoy, F., Wong, D.T.: Saliva diagnostics-current views and directions. Exp. Biol. Med. 242(5), 459–472 (2017)
Kaushik, A., Mujawar, M.A.: Point of care sensing devices: better care for everyone (2018)
Lins, A., Muniz, M., Bastos-Filho, C.J.: Comparing machine learning techniques for dementia diagnosis. In: 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1–6. IEEE (2018)
Lins, A., Muniz, M., Garcia, A., Gomes, A., Cabral, R., Bastos-Filho, C.J.: Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals. Comput. Methods Programs Biomed. 152, 93–104 (2017)
Loo, J., Yan, W., Ramachandran, P., Wong, D.: Comparative human salivary and plasma proteomes. J. Dent. Res. 89(10), 1016–1023 (2010)
Rosa, N., et al.: From the salivary proteome to the oralome: comprehensive molecular oral biology. Arch. Oral Biol. 57(7), 853–864 (2012)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Sabbagh, B., Mindt, S., Neumaier, M., Findeisen, P.: Clinical applications of MS-based protein quantification. PROTEOMICS-Clin. Appl. 10(4), 323–345 (2016)
Uddin, S., Khan, A., Hossain, M.E., Moni, M.A.: Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 19(1), 1–16 (2019). https://doi.org/10.1186/s12911-019-1004-8
Wang, X., Kaczor-Urbanowicz, K.E., Wong, D.T.W.: Salivary biomarkers in cancer detection. Med. Oncol. 34(1), 1–8 (2016). https://doi.org/10.1007/s12032-016-0863-4
Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)
Ackownledgments
Thanks are due to FCT/MCTES, for the financial support of the Center for Interdisciplinary Research in Health (UID/MULTI/4279/2019). Thanks are also due to FCT and UCP for the CEEC institutional financing of AC Esteves. This study was financed in part by the Coordenaçǎo de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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Soares, A., Esteves, E., Rosa, N., Esteves, A.C., Lins, A., Bastos-Filho, C.J.A. (2020). An Analysis of Protein Patterns Present in the Saliva of Diabetic Patients Using Pairwise Relationship and Hierarchical Clustering. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_14
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DOI: https://doi.org/10.1007/978-3-030-62362-3_14
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