网络解决方案在生物问题上的挑战与机遇。
Challenges and opportunities in network-based solutions for biological questions.
机构信息
Stanford Program in Biomedical Informatics, Stanford University, Stanford, CA, USA.
Program in Epithelial Biology, Stanford University, Stanford, CA, USA.
出版信息
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab437.
Network biology is useful for modeling complex biological phenomena; it has attracted attention with the advent of novel graph-based machine learning methods. However, biological applications of network methods often suffer from inadequate follow-up. In this perspective, we discuss obstacles for contemporary network approaches-particularly focusing on challenges representing biological concepts, applying machine learning methods, and interpreting and validating computational findings about biology-in an effort to catalyze actionable biological discovery.
网络生物学对于模拟复杂的生物现象很有用；随着基于图的新型机器学习方法的出现，它引起了人们的关注。然而，网络方法的生物学应用往往受到后续研究不足的困扰。在这个观点中，我们讨论了当代网络方法的障碍——特别是在表示生物学概念、应用机器学习方法以及解释和验证关于生物学的计算发现方面的挑战——以努力促进可操作的生物学发现。
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