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

• Published in: Symmetry (Basel) Vol. 15; no. 1; p. 42
• Main Authors: Martins, Jorge Emanuel, D’Alimonte, Davide, Simões, Joana, Sousa, Sara, Esteves, Eduardo, Rosa, Nuno, Correia, Maria José, Simões, Mário, Barros, Marlene
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Analysis; Automation; Bioinformatics; Biomarkers; Capillary electrophoresis; Colleges & universities; Data acquisition; Data analysis; Data mining; Data processing; Electrophoresis; Graphical user interface; Hypotheses; Integrated software; Machine learning; Mental health; Metadata; Modelling; Molecular biology; Molecular weight; Nervous system; Pattern recognition; Proteins; Scientific visualization; Symmetry; User interface; Visualization; Visualization (Computers); Young adults; Portugal
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym15010042

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.