Development of a Robust DNA Sequencing Multiclass Hybrid Classifier Employing Machine Learning and NLP For Automated Gene Classification
Sequencing DNA approaches figuring out the order of the quad chemical constructing blocks - called bases - that make up the DNA molecule. The collection makes scientists take notice of the sort of genetic statistics which are carried in a specific DNA segment. Scientists can use collection statistics to decide which stretches the DNA to incorporate genes and which stretch deliver regulatory instructions, turning genes on or off. In addition, and more importantly, sequencing the data can spotlight modifications in a gene which could cause disease. In this study, we have built a hybrid ensemble machine learning model using eleven classifiers for the development of a DNA sequence genomic classifier using machine learning and genomic sequencing in NLP through k-mers for the best possible results. We constructed a hybrid model using NLP and machine learning to progress the gene classification rapidly, which helped in increased research of biomedical science. In the later part of the study, we built a hybrid model employing stack ensemble methodology. To assess the model’s performance, we employed several performance evaluation measures. The hybrid model built on eleven classifiers gave an extremely high accuracy for three different sets of data, i.e., human, chimpanzee and dog. We did a comprehensive study on all three species, and investigated their genome classifications, to build a best fit model for them. The model for human data, reached 98.08% accuracy and an almost perfect AUC score. 90.75% accuracy was received for chimpanzee data, while 70% for dog data, making our hybrid ensemble transgress species.