Show simple item record

AuthorChocholova E.
AuthorBertok T.
AuthorJane E.
AuthorLorencova L.
AuthorHolazova A.
AuthorBelicka L.
AuthorBelicky S.
AuthorMislovicova D.
AuthorVikartovska A.
AuthorImrich R.
AuthorKasak P.
AuthorTkac J.
Available date2019-09-30T07:43:37Z
Publication Date2018
Publication NameClinica Chimica Acta
AbstractIn this study, one hundred serum samples from healthy people and patients with rheumatoid arthritis (RA) were analyzed. Standard immunoassays for detection of 10 different RA markers and analysis of glycan markers on antibodies in 10 different assay formats with several lectins were applied for each serum sample. A dataset containing 2000 data points was data mined using artificial neural networks (ANN). We identified key RA markers, which can discriminate between healthy people and seropositive RA patients (serum containing autoantibodies) with accuracy of 83.3%. Combination of RA markers with glycan analysis provided much better discrimination accuracy of 92.5%. Immunoassays completely failed to identify seronegative RA patients (serum not containing autoantibodies), while glycan analysis correctly identified 43.8% of these patients. Further, we revealed other critical parameters for successful glycan analysis such as type of a sample, format of analysis and orientation of captured antibodies for glycan analysis. 2018 Elsevier B.V.
SponsorFinancial support received from the Slovak Scientific Grant Agency VEGA 2/0137/18 and Slovak Research and Development Agency APVV 14-0753 is acknowledged. The research received funding from the European Research Council (No. 311532 ). This publication was made possible by NPRP grant no. 6-381-1-078 from the Qatar National Research Fund. This publication is the result of the project implementation: Centre for materials, layers and systems for applications and chemical processes under extreme conditions � Stage I, ITMS No.: 26240120007, supported by the ERDF. Appendix A
PublisherElsevier B.V.
dc.source Scopus
SubjectFeedforward artificial neural network
SubjectMachine learning algorithm
SubjectRheumatoid arthritis
TitleGlycomics meets artificial intelligence – Potential of glycan analysis for identification of seropositive and seronegative rheumatoid arthritis patients revealed
Volume Number481

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record