Analysis of DNA Sequence Classification Using CNN and Hybrid Models
Hemalatha Gunasekaran, K. Ramalakshmi, A. Rex Macedo Arokiaraj, S. Deepa Kanmani, Chandran Venkatesan, C. Suresh Gnana Dhas
Abstract
In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>K</a:mi> </a:math> -mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>K</c:mi> </c:math> -mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.