Bioinformatics data mining tool using data collected from red blood cells hemolysate

Rafea, Mahmoud; Zaki, Heba; Sultan, Torky I.

doi:10.1109/icctd.2010.5645850

Cited by 2 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Rafea

Elkafrawy

Nasef

et al. 2019

Front. Mol. Biosci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

“…This helps to monitor the treatment of these diseases conditions. Hence, in Rafea et al (2010) and Rafea and Souchelnytskyi (2012) they proposed a technique to discover biomarkers of diseases based on EDAS. However, they did not show which disease or set of diseases can be applied?…”

Section: Introductionmentioning

confidence: 99%