Zitnik Marinka, Li Michelle M, Wells Aydin, Glass Kimberly, Morselli Gysi Deisy, Krishnan Arjun, Murali T M, Radivojac Predrag, Roy Sushmita, Baudot Anaïs, Bozdag Serdar, Chen Danny Z, Cowen Lenore, Devkota Kapil, Gitter Anthony, Gosline Sara J C, Gu Pengfei, Guzzi Pietro H, Huang Heng, Jiang Meng, Kesimoglu Ziynet Nesibe, Koyuturk Mehmet, Ma Jian, Pico Alexander R, Pržulj Nataša, Przytycka Teresa M, Raphael Benjamin J, Ritz Anna, Sharan Roded, Shen Yang, Singh Mona, Slonim Donna K, Tong Hanghang, Yang Xinan Holly, Yoon Byung-Jun, Yu Haiyuan, Milenković Tijana
Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States.
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States.
Bioinform Adv. 2024 Aug 14;4(1):vbae099. doi: 10.1093/bioadv/vbae099. eCollection 2024.
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. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with 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. In more detail, 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. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.
Not applicable.
网络生物学是一个跨学科领域，连接了计算科学和生物科学，已被证明在推动跨生物系统和尺度理解细胞功能和疾病方面起着关键作用。尽管该领域已经存在了二十年，但仍处于起步阶段。它经历了快速发展，同时也伴随着新出现的挑战。这些挑战源于多种因素，特别是数据的复杂性和体量不断增加，以及描述不同生物组织层次的数据类型的多样性不断提高。我们讨论了网络生物学中当前的研究方向，重点是分子/细胞网络，但也涉及其他生物网络类型，如生物医学知识图谱、患者相似性网络、脑网络以及与疾病传播相关的社会/接触网络。更详细地说，我们强调了生物网络的推理和比较、多模态数据整合与异构网络、高阶网络分析、网络上的机器学习以及基于网络的个性化医学等领域。在概述了这五个领域的近期突破之后，我们对网络生物学的未来方向提出了展望。此外，我们还讨论了科学界、教育举措以及在该领域促进多样性的重要性。本文为网络生物学的近期和长期愿景制定了路线图。
不适用。
