Journal article

Computational network biology: Data, models, and applications

  • Liu, Chuang Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China
  • Ma, Yifang Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China
  • Zhao, Jing Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
  • Nussinov, Ruth Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA - Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
  • Zhang, Yi-Cheng Department of Physics, University of Fribourg, Fribourg, CH 1700, Switzerland - Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China
  • Cheng, Feixiong Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA - Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA - Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
  • Zhang, Zi-Ke College of Media and International Culture, Zhejiang University, Hangzhou 310028, China - Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China
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  • Physics Reports. - 2020, vol. 846, p. 1–66
English Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. Benefiting from the advances of network science and high-throughput biomedical technologies, studying the biological systems from network biology has attracted much attention in recent years, and networks have long been central to our understanding of biological systems, in the form of linkage maps among genotypes, phenotypes, and the corresponding environmental factors. In this review, we summarize the recent developments of computational network biology, first introducing various types of biological networks and network structural properties. We then review the network-based approaches, ranging from some network metrics to the complicated machine-learning methods, and emphasize how to use these algorithms to gain new biological insights. Furthermore, we highlight the application in neuroscience, human disease, and drug developments from the perspectives of network science, and we discuss some major challenges and future directions. We hope that this review will draw increasing interdisciplinary attention from physicists, computer scientists, and biologists.
Faculté des sciences et de médecine
Département de Physique
  • English
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