In the domain of network biology, the interactions among heterogeneous
genomic and molecular entities are represented through networks. Link
prediction (LP) methodologies are instrumental in inferring missing or
prospective associations within these biological networks. In this review, we
systematically dissect the attributes of local, centrality, and embedding-based
LP approaches, applied to static and dynamic biological networks. We undertake
an examination of the current applications of LP metrics for predicting links
between diseases, genes, proteins, RNA, microbiomes, drugs, and neurons. We
carry out comprehensive performance evaluations on established biological
network datasets to show the practical applications of standard LP models.
Moreover, we compare the similarity in prediction trends among the models and
the specific network attributes that contribute to effective link prediction,
before underscoring the role of LP in addressing the formidable challenges
prevalent in biological systems, ranging from noise, bias, and data sparseness
to interpretability. We conclude the review with an exploration of the
essential characteristics expected from future LP models, poised to advance our
comprehension of the intricate interactions governing biological systems.

网络生物学中，基于网络结构表示异质基因组和分子实体之间的相互作用。本综述系统剖析了应用于静态和动态生物网络的局部、中心性和嵌入式链接预测方法的属性，并在疾病、基因、蛋白质、RNA、微生物组、药物和神经元之间的预测链接方面考察了当前链接预测度量的应用。我们对已建立的生物网络数据集进行了全面的性能评估，展示了标准链接预测模型的实际应用。此外，我们比较了各模型之间预测趋势的相似性以及对于有效链接预测所起作用的特定网络属性，最后强调了链接预测在解决生物系统中普遍存在的噪音、偏见、数据稀疏性和可解释性等难题方面的作用。我们通过探索未来链接预测模型的必要特征，以推进对控制生物系统的复杂相互作用的理解来总结这篇综述。

网络生物学中链路预测应用评述

A Review of Link Prediction Applications in Network Biology

Network biology has been successfully used to help reveal complex mechanisms
of disease, especially cancer. On the other hand, network biology requires
in-depth knowledge to construct disease-specific networks, but our current
knowledge is very limited even with the recent advances in human cancer
biology. Deep learning has shown a great potential to address the difficult
situation like this. However, deep learning technologies conventionally use
grid-like structured data, thus application of deep learning technologies to
the classification of human disease subtypes is yet to be explored. Recently,
graph based deep learning techniques have emerged, which becomes an opportunity
to leverage analyses in network biology. In this paper, we proposed a hybrid
model, which integrates two key components 1) graph convolution neural network
(graph CNN) and 2) relation network (RN). We utilize graph CNN as a component
to learn expression patterns of cooperative gene community, and RN as a
component to learn associations between learned patterns. The proposed model is
applied to the PAM50 breast cancer subtype classification task, the standard
breast cancer subtype classification of clinical utility. In experiments of
both subtype classification and patient survival analysis, our proposed method
achieved significantly better performances than existing methods. We believe
that this work is an important starting point to realize the upcoming
personalized medicine.

本文提出了一种混合模型，该模型将图卷积神经网络和关系网络组合在一起，在乳腺癌亚型分类和患者存活分析的实验中，该模型表现出了比现有方法更好的性能。