Predicting and Characterizing piRNAs and their functions Using an Integrated Machine Learning Approach

Abstract

One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%. One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%. One class of short non-coding RNA molecule that is well recognized is called PIWI-interacting RNA (piRNA). PiRNAs are involved in the creation of novel medications as well as the identification of different kinds of tumors. Additionally, it is associated with stopping transposes, managing gene transcription, and maintaining genomic integrity. The important role that piRNAs play in biological processes has led to a growing body of research in bioinformatics on the discovery of piRNAs and their functionality. In this research, a powerful model is proposed to improve PiRNA prediction and functionality. The suggested model uses four classifiers (Logistic Regression, SVC, Random Forest, and Gradient Boosting Classifier) for classification. Moreover, TNC and DNC are used to acquire features. There are two layers involved in developing the suggested model. A sequence's potential to be piRNA is predicted in the first layer, and its potential to direct target mRNA deadenylation is predicted in the second. In the first layer, the model's accuracy is 98.59%, and in the second layer, it is 94.55%.