Pattern Identification on Protein Sequences of Neurodegenerative Diseases Using Association Rule Mining

M Shahedul, Islam and Md. Abul Kashem, Mia and Mohammad, Shamsur Rahman and Swapnil, Saha (2018) Pattern Identification on Protein Sequences of Neurodegenerative Diseases Using Association Rule Mining. In: Seventh International Conference on Advances in Computing, Electronics and Communication-ACEC2018, 18-19 August, 2018, Kuala Lumpur, Malaysia.

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Proteins are the integral part of all living beings, which are building blocks of many amino acids. To be functionally active, amino acids chain folds up in a complex way to give each protein an unique 3D shape, where a minor error may cause misfolded structure. Neurodegenerative diseases e.g. Alzheimer, Parkinson, Sickle cell anemia, etc. arise due to misfolding in protein sequences. Thus, identifying the patterns of the amino acids is important for inferring the protein associated genetic diseases. Recent studies in predicting patterns of amino acids focused on only the simple chronic neurodegenerative disease Chromaffin Tumor by applying association rule mining. However, more complex diseases are yet to be attempted. Moreover, the association rules obtained by these studies were not verified by usefulness measuring tools. In this paper, we have analyzed the protein sequences associated with more complex neurodegenerative protein misfolded diseases by association rule mining technique, where only the useful rules are finally sorted out with the use of interestingness measures. Adopting the quantitative experimental method, our work forms more reliable and strong association rules among the most domination amino acids of corresponding misfolded proteins and identify the dominating patterns of amino acid of complex genetic disease.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: amino acid, association rule, disease, frequent pattern, protein misfolding, protein sequence.
Depositing User: Mr. John Steve
Date Deposited: 07 Mar 2019 15:37
Last Modified: 07 Mar 2019 15:37

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