Data mining is the practise of discovering connections between seemingly unrelated pieces of biological information. Rapid progress in genomics and proteomics in recent years has resulted in an abundance of biological data. Thus, categorising biological sequences and structures according to essential properties and functions is a pressing issue in the field of biological data processing. Many methods have been used to generate recurrent patterns from published works for use in a wide range of contexts. The frequency with which this algorithm was produced has diminished. Because of this, it's completely pointless. In this case, I want to use two different methods to compare the common pattern and optimise the data. Hence, we find it to be of great value. The contaminated protein sequence is the root cause of several human illnesses, and our method is designed to extract the amino acids that are both hidden and most dominant in the sequence. We deal with this issue by employing a combination of the apriori algorithm, the genetic algorithm, and strong association rules for pattern prediction. Apply fuzzy logic to the optimisation of data and the identification of intriguing common patterns in the protein sequence database. This Recurring Pattern is quite helpful in the Pharmaceutical Industry.
Data sharing is not relevant to this topic since no datasets were created or analyzed over the course of this investigation.
Each author confirms that they have no competing interests.
Krupali Patel participated in the conception and execution of the study, the analysis and interpretation of the data, and the drafting of the paper. Pravinbhai managed the work on this project.
No funding was provided to the author(s) of this article during its research, writing, or publishing.
I have used Rapid miner.
I owe a great debt of appreciation to Pravinbhai, my primary supervisor, who gave me invaluable direction throughout this endeavour. I'd also want to say thanks to the friends and family members that helped me during this process and provided invaluable feedback and insights.
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