Paper Title
ACCELERATING BIOLOGICAL NETWORK ANALYSIS WITH DEEP LEARNING

Abstract
The introduction of machine learning techniques into biological research is becoming imperative, as these techniques simplify the process of building predictive models from large datasets derived from biological systems. The field of machine learning, particularly deep learning algorithms such as artificial neural network, has great promise in the process of deciphering complicated patterns seen in complex biological systems. This makes it a useful tool in the fields like biomedical research and cellular biology. This present study provides an overview of deep learning techniques and their cutting-edge applications in the field of biomedicine. The interaction of machine learning and network biology is emphasized, with a focus on their effects on different domains such as drug discovery, disease biology, synthetic biology, and microbiome research. The characteristics of biological networks and artificial neural networks are carefully analyzed, evaluated, and compared. Many elements are examined, such as anatomical traits, neuronal properties, learning capacities, computational styles, and error tolerance. There is also a discussion of graph neural networks and its expanding uses in bioinformatics fields, including in silico drug discovery and protein function and interaction prediction. Furthermore, new applications of deep learning in solving enduring problems like automated disease diagnosis and data-driven gene interaction prediction are highlighted. Keywords - Artificial Neural Networks, Biological Neural Networks, Deep Learning, Machine Learning, Network Analysis