SalivaPRINT Toolkit – Protein profile evaluation and phenotype stratification

Cruz, I. de Gispert; Esteves, Eduardo; Fernandes, Mónica; Rosa, Nuno; Correia, Maria José; Arrais, Joel P.; Barros, Marlene

doi:10.1016/j.jprot.2017.08.011

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Similarly, to optimize the analysis of the numerous data generated by the Experion™ system, we wrote an algorithm using the programming platform MATLAB for data visualization, exploration, and hypothesis-driven biomarker research. Researchers at SalivaTec Laboratory, have recently proposed bioinformatics solutions [12,13] to address the main problem of this study. Hence, this approach aims (i) to complement the already published results and (ii) to address other specific difficulties, i.e., profiling mental health.…”

Section: Introductionmentioning

confidence: 99%

MODeLING.Vis: A Graphical User Interface Toolbox Developed for Machine Learning and Pattern Recognition of Biomolecular Data

Martins

D’Alimonte²,

Simões

et al. 2022

Symmetry

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Many scientific publications that affect machine learning have set the basis for pattern recognition and symmetry. In this paper, we revisit the concept of “Mind-life continuity” published by the authors, testing the symmetry between cognitive and electrophoretic strata. We opted for machine learning to analyze and understand the total protein profile of neurotypical subjects acquired by capillary electrophoresis. Capillary electrophoresis permits a cost-wise solution but lacks modern proteomic techniques’ discriminative and quantification power. To compensate for this problem, we developed tools for better data visualization and exploration in this work. These tools permitted us to examine better the total protein profile of 92 young adults, from 19 to 25 years old, healthy university students at the University of Lisbon, with no serious, uncontrolled, or chronic diseases affecting the nervous system. As a result, we created a graphical user interface toolbox named MODeLING.Vis, which showed specific expected protein profiles present in saliva in our neurotypical sample. The developed toolbox permitted data exploration and hypothesis testing of the biomolecular data. In conclusion, this analysis offered the data mining of the acquired neuroproteomics data in the molecular weight range from 9.1 to 30 kDa. This molecular weight range, obtained by pattern recognition of our dataset, is characteristic of the small neuroimmune molecules and neuropeptides. Consequently, MODeLING.Vis offers a machine-learning solution for probing into the neurocognitive response.

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Section: Introductionmentioning

confidence: 99%

MODeLING.Vis: A Graphical User Interface Toolbox Developed for Machine Learning and Pattern Recognition of Biomolecular Data

Martins

D’Alimonte²,

Simões

et al. 2022

Symmetry

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“…We recently described an automated capillary electrophoresis-based strategy that allows one to obtain a salivary protein profile -the SalivaPrint Toolkit [4,7]. Since proteins are separated according to their molecular mass, changes in peak morphology or fluorescence intensity (translated by changes in peak height) correspond to fluctuations in the proteins' concentration or the type of proteins being expressed.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Protein Patterns Present in the Saliva of Diabetic Patients Using Pairwise Relationship and Hierarchical Clustering

Soares

Esteves

Rosa

et al. 2020

Lecture Notes in Computer Science

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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.

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Classification of patients with diabetes by analyzing the patterns of proteins present in saliva using machine learning

Costa

Bastos-Filho

Lins³

2022

2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

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SalivaPRINT Toolkit – Protein profile evaluation and phenotype stratification

Cited by 3 publications

References 14 publications

MODeLING.Vis: A Graphical User Interface Toolbox Developed for Machine Learning and Pattern Recognition of Biomolecular Data

MODeLING.Vis: A Graphical User Interface Toolbox Developed for Machine Learning and Pattern Recognition of Biomolecular Data

An Analysis of Protein Patterns Present in the Saliva of Diabetic Patients Using Pairwise Relationship and Hierarchical Clustering

Classification of patients with diabetes by analyzing the patterns of proteins present in saliva using machine learning

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