Abstract

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent, having witnessed rapid evolution accompanied by emerging challenges stemming from the growing complexity and volume of data and the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. We highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine, and offer a perspective on the future directions of network biology.

Citation

Zitnik M, Li MM, Wells A, Glass K, Morselli Gysi D, [et al.], Milenkovic T. Current and future directions in network biology. Bioinformatics Advances. 2024;4(1):vbae099. https://doi.org/10.1093/bioadv/vbae099