Genomics at the Nexus of AI, Computer Vision, and Machine Learning

10Unraveling Biological Complexity: Leveraging Deep Learning Models for Precise Classification and Understanding of Protein Types and Functions

Swathi Gowroju^1*, M. Sudhakar², Mohit³ and Turki Aljrees⁴

¹AI&ML Department, Sreyas Institute of Engineering and Technology, Hyderabad, India

²CSE Department, Sreyas Institute of Engineering and Technology, Hyderabad, India

³Research Scholar, Institute of Management Studies and Research, Maharshi Dayanand University, Rohtak, India

⁴Department College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia

Abstract

Identifying and classifying protein kinds and activities within biological systems is complex and challenging. Traditional classification methods often offer limited precision due to the complexity of proteins. This chapter explores the use of deep learning (DL) models, specifically variational autoencoders (VAE) that incorporate Kullback-Leibler (KL) divergence, to understand and solve this intricate problem. Proteins, which have various physical characteristics, are frequently classified broadly. However, a more detailed classification is achieved by considering the distinct properties of individual amino acids. By examining the amino acid sequences, proteins are accurately classified, as the specific arrangement within the chain intimately determines their functioning. Based on this idea, a framework that combines recurrent neural networks (RNN) and long short-term memory (LSTM) models ...