A Theoretical Approach for Correlating Proteins to Malignant Diseases

Frontiers in Molecular Biosciences
Rasha ElnemrAmani Tariq Jamal

Abstract

Malignant Tumors are developed over several years due to unknown biological factors. 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 prot...Continue Reading

References

Jan 20, 2018·PloS One·Iyad AqraKim-Kwang Raymond Choo
Apr 20, 2019·Frontiers in Molecular Biosciences·Mahmoud RafeaAmani Tariq Jamal

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ITDApriori
ECLAT
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