2022 • In BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings
Mass Spectrometry; Proteomics; SomaScan; Systemic Autoinflammatory Diseases; Conventional machines; Disease classification; False discovery rate; Machine learning algorithms; Predictive capabilities; Proteomic approaches; Separate analysis; Somascan; Systemic autoinflammatory disease; Artificial Intelligence; Computer Science Applications; Information Systems; Information Systems and Management; Biomedical Engineering; Instrumentation
Abstract :
[en] A cross-analysis study was conducted to compare proteomic platforms in classifying patients with Systemic Autoinflammatory diseases, using proteins extracted from different profiling experiments. The datasets used were obtained from SomaScan assays and Mass Spectrometry (MS). A separate analysis was performed to each dataset based on the false discovery rate (FDR) in order to extract statistically important proteins. Conventional machine learning algorithms were subsequently employed to evaluate the denoted proteins as candidate biomarkers and compare the predictive capabilities of the two proteomic platforms. Using the SomaScan assay, we managed to achieve higher classification metrics compared to the MS dataset. An improvement was also attained on the classification results when the features used were extracted from the MS data and applied on the SomaScan dataset, compared to the opposite combination. Finally, the proteins derived from the FDR analysis in both datasets proved to be highly correlated regarding their importance score.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Papagiannopoulos, Orestis D.; University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, Ioannina, Greece
Papaloukas, Costas; Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology (FORTH-IMBB), Dept. of Biological Applications and Technology, University of Ioannina, Dept. of Biomedical Research, Ioannina, Greece
Pezoulas, Vasileios C.; University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, Ioannina, Greece
Van De Werken, Harmen J.G.; Erasmus MC Cancer Institute, University Medical Center, Dept. of Immunology, Rotterdam, Netherlands
Poulet, Christophe ; Centre Hospitalier Universitaire de Liège - CHU > > Service de rhumatologie ; Laboratory of Rheumatology, GIGA-Research CHULiege, Liege, Belgium
Mueller, Yvonne M.; Erasmus MC Cancer Institute, University Medical Center, Dept. of Immunology, Rotterdam, Netherlands
Katsikis, Peter D.; Erasmus MC Cancer Institute, University Medical Center, Dept. of Immunology, Rotterdam, Netherlands
de Seny, Dominique ; Centre Hospitalier Universitaire de Liège - CHU > > Service de rhumatologie ; Erasmus MC Cancer Institute, University Medical Center, Dept. of Immunology, Rotterdam, Netherlands
Fotiadis, Dimitrios I.; Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology (FORTH-IMBB), Dept. of Biological Applications and Technology, University of Ioannina, Dept. of Biomedical Research, Ioannina, Greece
Language :
English
Title :
Comparison of Proteomic Approaches in Autoinflammatory Disease Classification
Publication date :
2022
Event name :
2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
Event place :
Ioannina, Grc
Event date :
27-09-2022
Audience :
International
Main work title :
BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings
Publisher :
Institute of Electrical and Electronics Engineers Inc.
IEEE IEEE Engineering in Medicine and Biology Society (EMBS) IEEE Open Journal of Engineering in Medicine and Biology (OJEMB) Medical Technology and Intelligent Information Systems (MEDLAB) Rizarios Foundation
Funding text :
This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 779295 (ImmunAID – Immunome project consortium for AutoInflammatory Disorders).This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 779295 (ImmunAID - Immunome project consortium for AutoInflammatory Disorders).
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