Frequent contiguous pattern mining over biological sequences of protein misfolded diseases

Mohammad Shahedul Islam, Md Abul Kashem Mia, Mohammad Shamsur Rahman, Mohammad Shamsul Arefin*, Pranab Kumar Dhar, Takeshi Koshiba

*この研究の対応する著者

研究成果: Article査読

抄録

Background: Proteins are 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 a unique 3D shape, where a minor error may cause misfolded structure. Genetic disorder diseases i.e. Alzheimer, Parkinson, etc. arise due to misfolding in protein sequences. Thus, identifying patterns of amino acids is important for inferring protein associated genetic diseases. Recent studies in predicting amino acids patterns focused on only simple protein misfolded disease i.e. Chromaffin Tumor, by association rule mining. However, more complex diseases are yet to be attempted. Moreover, association rules obtained by these studies were not verified by usefulness measuring tools. Results: In this work, we analyzed protein sequences associated with complex protein misfolded diseases (i.e. Sickle Cell Anemia, Breast Cancer, Cystic Fibrosis, Nephrogenic Diabetes Insipidus, and Retinitis Pigmentosa 4) by association rule mining technique and objective interestingness measuring tools. Experimental results show the effectiveness of our method. Conclusion: Adopting quantitative experimental methods, this work can form more reliable, useful and strong association rules i. e. dominating patterns of amino acid of complex protein misfolded diseases. Thus, in addition to usual applications, the identified patterns can be more useful in discovering medicines for protein misfolded diseases and thereby may open up new opportunities in medical science to handle genetic disorder diseases.

本文言語English
論文番号435
ジャーナルBMC Bioinformatics
22
1
DOI
出版ステータスPublished - 2021 12

ASJC Scopus subject areas

  • 構造生物学
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 応用数学

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