Rediscovering Red Blood Cells: Revealing Their Dynamic Antigens Store and Its Role in Health and Disease

Rafea, Mahmoud; Souchelnytskyi, Serhiy

doi:10.5772/48593

Cited by 3 publications

(4 citation statements)

References 11 publications

(9 reference statements)

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“…One of the important areas of research today is attempting to identify the association between the habits and behavior of an individual and diseases, specifically, Malignant Tumor. Rafea and Souchelnytskyi (2012) observed and described phenomena related to the protein content of the Red Blood Cell (RBC). It was noticed that plasma contains antibodies against some of the RBC proteins, which are contained within the cytoplasm of RBC of the same person.…”

Section: Introductionmentioning

confidence: 99%

A Theoretical Approach for Correlating Proteins to Malignant Diseases

Elnemr¹,

Nasef

Elkafrawy

et al. 2020

Front. Mol. Biosci.

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These biological factors induce changes in the body and consequently, they lead to Malignant Tumors. Some habits and behaviors initiate these biological factors. In effect, the immune system cannot recognize a Malignant Tumor as foreign tissue. In order to discover a fascinating pattern of these habits, behaviors, and diseases and to make effective decisions, different machine learning techniques should be used. This research attempts to find the association between normal proteins (environmental factors) and diseases that are difficult to diagnose and propose justifications for those diseases. This paper proposes a technique for medical data mining using association rules. The proposed technique overcomes some of the limitations in current association algorithms such as the Apriori algorithm and the Equivalence CLAss Transformation (ECLAT) algorithm. A modification to the Apriori algorithm has been proposed to mine Erythrocytes Dynamic Antigens Store (EDAS) data in a more efficient and tractable way. The experiments inferred that there is a relation between normal proteins as environment proteins, food proteins, commensal proteins, tissue proteins, and disease proteins. Also, the experiments show that habits and behaviors are associated with certain diseases. The presented tool can be used in clinical laboratories to discover the biological causes of malignant diseases.

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Section: Introductionmentioning

confidence: 99%

A Theoretical Approach for Correlating Proteins to Malignant Diseases

Elnemr¹,

Nasef

Elkafrawy

et al. 2020

Front. Mol. Biosci.

Self Cite

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“…Rafea and Souchelnytskyi (2012) observed and described a phenomenon related to the protein content of the Red Blood Cell (RBC). It was noticed that the plasma contains antibodies against some of RBC proteins, which are contained within RBC cytoplasm of the same person.…”

Section: Introductionmentioning

confidence: 99%

“…A random generation of EDAS was described in Rafea et al (2010) and Rafea and Souchelnytskyi (2012). Meanwhile, this generation of the EDAS model was very simple and did not reflect the real EDAS.…”

Section: Introductionmentioning

confidence: 99%

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Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Rafea

Elkafrawy

Nasef

et al. 2019

Front. Mol. Biosci.

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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.

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