TY - JOUR
T1 - Frequent contiguous pattern mining over biological sequences of protein misfolded diseases
AU - Islam, Mohammad Shahedul
AU - Mia, Md Abul Kashem
AU - Rahman, Mohammad Shamsur
AU - Arefin, Mohammad Shamsul
AU - Dhar, Pranab Kumar
AU - Koshiba, Takeshi
N1 - Funding Information:
This paper is a revised and extended version of a paper entitled “Pattern Identification on Protein Sequences of Neurodegenerative Diseases Using Association Rule Mining” presented at Proceedings of the Seventh International Conference on Advances in Computing, Electronics and Communication (ACEC 2018) , Kuala Lumpur, Malaysia on 18–19 August 2018
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Amino acid
KW - Association rule
KW - Disease
KW - Frequent pattern
KW - Protein misfolding
KW - Protein sequence
UR - http://www.scopus.com/inward/record.url?scp=85114720068&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114720068&partnerID=8YFLogxK
U2 - 10.1186/s12859-021-04341-y
DO - 10.1186/s12859-021-04341-y
M3 - Article
C2 - 34511072
AN - SCOPUS:85114720068
SN - 1471-2105
VL - 22
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - 1
M1 - 435
ER -