Repository logo
 
Publication

MODeLING.Vis: a graphical user interface toolbox developed for machine learning and pattern recognition of biomolecular data

2023-01Journal article Open access
Simple item page
dc.contributor.authorMartins, Jorge Emanuel
dc.contributor.authorD’Alimonte, Davide
dc.contributor.authorSimões, Joana
dc.contributor.authorSousa, Sara
dc.contributor.authorEsteves, Eduardo
dc.contributor.authorRosa, Nuno
dc.contributor.authorCorreia, Maria José
dc.contributor.authorSimões, Mário
dc.contributor.authorBarros, Marlene
dc.date.accessioned2023-01-04T09:29:39Z
dc.date.available2023-01-04T09:29:39Z
dc.date.issued2023-01
dc.description.abstractMany 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/sym15010042pt_PT
dc.identifier.eid85146754250
dc.identifier.issn2073-8994
dc.identifier.urihttp://hdl.handle.net/10400.14/39713
dc.identifier.wos000918983900001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCognitionpt_PT
dc.subjectData-miningpt_PT
dc.subjectData explorationpt_PT
dc.subjectData visualizationpt_PT
dc.subjectGUI toolboxpt_PT
dc.subjectMachine learningpt_PT
dc.subjectMolecular stratificationpt_PT
dc.subjectPattern recognitionpt_PT
dc.subjectSymmetrypt_PT
dc.titleMODeLING.Vis: a graphical user interface toolbox developed for machine learning and pattern recognition of biomolecular datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1pt_PT
oaire.citation.titleSymmetrypt_PT
oaire.citation.volume15pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
symmetry_15_00042_v2.pdf
Size:
5.12 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.44 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

CIIS - Contribuições em Revistas Científicas / Contribution to Journals