TNFR-LSTM: A Deep Intelligent Model for Identification of Tumour Necroses Factor Receptor (TNFR) Activity

Abstract

Tumour necrosis factors (TNFs) are key players in processes such as inflammation, cancer development, and autoimmune diseases. However, accurately identifying TNFs remains challenging because of their complex interactions with other cytokines. Although existing machine learning models offer some potential, they often fall short in reliably distinguishing TNFs. To address this issue, the authors developed DEEP-TNFR, a more advanced model designed specifically to predict TNFR activity. The approach incorporates features such as relative and reverse positions, along with statistical moments, and is tested on a recognised benchmark dataset. The authors explored six different deep learning classifiers, including fully connected networks (FCN), convolutional neural networks (CNN), simple RNN (RNN), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent units (GRU). The model's effectiveness was evaluated through multiple methods: self-consistency, independent set testing, and 5- and 10-fold cross-validation, using metrics, such as accuracy, specificity, sensitivity, and Matthews correlation coefficient. Among these classifiers, LSTM proved to be the most effective, outperforming the others and setting a new standard compared to previous studies. DEEP-TNFR is poised to significantly support ongoing research by enhancing the accuracy of TNFR identification.