DNA Sequence Identification via Biologically Guided Feature Engineering and Hybrid ML–LSTM Networks
The promoter is the part of DNA, which is responsible of initiating RNA polymerase transcription of a gene. The location of this part of DNA is upstream the transcription start site. According to researches, the genetic promotors contribute majorly in many human diseases such as cancer, diabetes and Huntington’s disease. Therefore, promotor detection corresponds as a very crucial task. In this study, a hypered detection system, which integrates biologically developed feature extraction with traditional machine learning (ML) algorithms in addition to use Long Short-Term Memory (LSTM) network as a deep learning approach, has been proposed. The dataset used includes 106 nucleotide sequences. Results obtained from the study show that the perfect performance across all metrics (accuracy, sensitivity, specificity, precision, and F1-score) has been achieved when Naive Bayes used as a classifier, which reach 100% and AUC=1.The confusion matrix analyses and ROC curve confirm that LSTM model achieved 100% training accuracy and 84.38% test accuracy. The architecture and performance of the proposed model make it applicable in IoT-based intelligent genomic and healthcare systems, which enabling real-time and remote promoter detection.
Volume & Issue
Vol. Volume 18 / Iss. Issue 2