Optimal prediction of viral host from genomic datasets using ensemble classifier

Viruses are common biological agents that are supposed to be the world's greatest repositories of undiscovered genetic diversity. One of the common problems in bioinformatics is gene-disease prediction. Techniques for taxonomic classification, host range, and biological properties of newly discovered viruses are needed for complete functional characterization and annotation. Understanding the behaviors as well as interactions of microbial populations needs research into virus-host infectious associations. The following three main steps of an unique viral host prediction method using genomic datasets are introduced in this research work: (a) Pre-processing, (b) Feature extraction, and (c) Prediction phase. In starting stage, raw genomic datasets are exposed to pre-processing, which would include data cleaning activities. The pre-processed data is then used to extract the statistical features, high order statistical features, weighted holoentropy, chi-squared features, relief -based features, and symmetric uncertainty-based features. The ensemble approach, which uses the Support Vector Machine (SVM), Neural Network (NN), Random Forest (RF), and Convolutional Neural Network (CNN), respectively, is then employed for the prediction. Here, SVM, NN, and RF classifiers are fed the retrieved features as input. These classifiers' outputs will be fed into an optimised CNN, which produces the final prediction outcome. Additionally, the Adaptive Flower Pollination Algorithm (AFPA), an upgraded variant of the con-ventional Flower Pollination Protocol, is used to fine-tune the weights of optimised CNN in order to increase prediction accuracy (FPA). The AFPA+EC's F1-Score is 0.75026, which is correspondingly 61%, 52.4%, 20.2%, and 26.5% better than the conventional methods SVM, KNN, RF, and CNN.

Automated summary

This research introduces a unique viral host prediction method using genomic datasets, including preprocessing, feature extraction, and prediction phase. The raw genomic datasets are preprocessed and various statistical features are extracted. The SVM, NN, RF, and CNN classifiers are then used for prediction, with an optimized CNN fine-tuned using the AFPA algorithm to increase accuracy.