IR-MBiTCN: Computational prediction of insulin receptor using deep learning: A multi-information fusion approach with multiscale bidirectional temporal convolutional network

Abstract

The insulin receptor (IR) is a transmembrane protein that controls glucose homeostasis and is highly associated with chronic diseases including cancer and neurological. Traditional experimental methods have provided essential insights into IR structure and function, but they are constrained by time, cost, and scalability. To address these limitations, we present a computational technique for IR prediction based on deep learning and multi-information fusion. First, we built sequence-based training and testing datasets. Second, the compositional, word embedding, and evolutionary features were retrieved using the Weighted-Group Dipeptide Composition (W-GDPC), FastText, and Bi-Block-Position Specific Scoring Matrix (BB-PSSM), respectively. Third, we use compositional, word embedding, and evolutionary features to generate multi-perspective fused features (MPFF). Fourth, the Multiscale Bidirectional Temporal Convolutional Network (MBiTCN) is used to train the model to process features at multiscale and analyze sequences in both forward and backward directions. The proposed approach (IR-MBiTCN) outperforms competing deep learning (DL) and machine learning (ML)-based models on training and testing datasets, achieving 83.50 % and 79.43 % accuracy, respectively. This study represents a pioneering use of computational methodology in IR prediction, providing a scalable, efficient alternative to experimental procedures and paving the way for advances in chronic disease therapy and drug discovery.