Leveraging Artificial Intelligence to Predict Tumor Necrosis Factor-Alpha Activity for Enhanced Disease Diagnosis and Therapeutic Development

Tumor Necrosis Factor-Alpha (TNF-α) is a key pro-inflammatory cytokine involved in autoimmune disorders, cancer, and chronic inflammatory diseases. Accurate prediction of TNF-α activity is vital for understanding disease mechanisms and advancing targeted therapies. This review highlights the emerging role of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, in predicting TNF-α activity. Recent advancements are discussed, including AI-based analysis of multi-omics data, clinical biomarkers, and patient records for disease diagnosis, treatment response prediction, and drug discovery. The AI models such as random forests and support vector machines have demonstrated improved accuracy in classifying TNF-α activity and supporting personalized treatment strategies. The integration of AI in inflammatory pathway modeling has accelerated the development of TNF-α inhibitors and optimized therapeutic outcomes. Despite these advancements, challenges remain in data standardization, model transparency, and ethical considerations. Future directions emphasize the need for incorporating real-world clinical data and enhancing model robustness to fully realize AI’s potential in TNF-α-related research and precision medicine.