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Front. Mol. Biosci. | doi: 10.3389/fmolb.2019.00019

Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

 Passent M. El-kafrawy1*, Mahmoud Rafea2*,  Mohamed Nasef1 and Rasha Elnemer2*
  • 1Menofia University, Egypt
  • 2Central Laboratory For Agricultural Expert Systems, Egypt

Erythrocytes Dynamic Antigens Store (EDAS) is a new discovery. EDAS consists of self-antigens and foreign (non-self) antigens. In patients with infectious diseases or malignancies, antigens of infection microorganism or malignant tumor exist in EDAS. Storing EDAS of normal individuals and patients in a database has, at least, two benefits. First, EDAS can be mined to determine biomarkers representing diseases which can enable researchers to develop a new line of laboratory diagnostic tests and vaccines. Second, EDAS can be queried, directly, to reach a precise diagnosis without the need to do many laboratory tests. The target is to find the minimum set of proteins that can be used as biomarkers for a particular disease. A hypothetical EDAS is created. Hundred-thousand records are randomly generated. The mathematical model of hypothetical EDAS together with the proposed techniques for biomarker discovery and direct diagnosis are described. The different possibilities that may occur in reality are experimented. Biomarkers’ proteins are identified for pathogens and malignancies, which can be used to diagnose conditions that are difficult to diagnose. The presented tool can be used in clinical laboratories to diagnose disease disorders.

Keywords: Mass Spectrometry, Disorders Diagnosis, Erythrocytes Dynamic Antigens Store (EDAS), biomarkers, Computer tools in clinics, mathematical model, machine learning

Received: 16 Jul 2018; Accepted: 07 Mar 2019.

Edited by:

Pier Paolo Piccaluga, University of Bologna, Italy

Reviewed by:

Nikolay M. Borisov, I.M. Sechenov First Moscow State Medical University, Russia
Ritesh K. Srivastava, University of Alabama at Birmingham, United States  

Copyright: © 2019 El-kafrawy, Rafea, Nasef and Elnemer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Passent M. El-kafrawy, Menofia University, Shibin Al Kawm, Al Minufiyah, Egypt, basant.elkafrawi@science.menofia.edu.eg
Prof. Mahmoud Rafea, Central Laboratory For Agricultural Expert Systems, Giza, Giza, Egypt, mahmoudrafea@arc.sci.eg
Mrs. Rasha Elnemer, Central Laboratory For Agricultural Expert Systems, Giza, Giza, Egypt, rashaelnemr@arc.sci.eg