Challenges and opportunities in network-based solutions for biological questions

被引：16

作者：

Guo, Margaret G. ^{[1
]}

Sosa, Daniel N. ^{[1
]}

Altman, Russ B. ^{[2
]}

机构：

[1] Stanford Univ, Biomed Informat, Stanford, CA 94305 USA

[2] Stanford Univ, Bioengn Genet Med Biomed Data Sci & Courtesy Comp, Stanford, CA 94305 USA

来源：

BRIEFINGS IN BIOINFORMATICS | 2022年 / 23卷 / 01期

基金：

美国国家卫生研究院;

关键词：

networks; knowledge graphs; embeddings; interpretability; biological validation; REGULATORY NETWORKS; INFERENCE; INTERACTOME; DISEASE; MODEL;

D O I：

10.1093/bib/bbab437

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

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