Application of deep learning in biological networks

This comparison shows the use of deep learning techniques in biological networks, focusing on diverse applications and research problems. The analysis includes various properties, such as results, application domains, classification tasks, dataset features, and machine learning methods/algorithms employed.
The research problems explored include multiclass disease classification, compound-protein interaction prediction, microRNA-disease association prediction, identifying essential proteins, predicting protein functions, robust phenotype prediction, disease outcome classification, drug-target association prediction, risk stratification modeling for lung cancer, and modeling polypharmacy side effects. Through this comparison, we aim to explain the effectiveness and adaptability of deep learning approaches in addressing complex biological challenges across different domains and applications within biological networks.