# A Review of Link Prediction Applications in Network Biology

arXiv: https://arxiv.org/abs/2312.01275 · HTML: https://mlsanity.com/paper/2312.01275/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 138 references that are in the map.
- First posted on arXiv 3 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [Stacking Models for Nearly Optimal Link Prediction in Complex Networks](https://mlsanity.com/paper/1909.07578/) (2019)
- [OpenBioLink: A benchmarking framework for large-scale biomedical link prediction](https://mlsanity.com/paper/1912.04616/) (2019)
- [DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data](https://mlsanity.com/paper/2107.02168/) (2021)
- [Progresses and Challenges in Link Prediction](https://mlsanity.com/paper/2102.11472/) (2021)
- [Link Prediction via Higher-Order Motif Features](https://mlsanity.com/paper/1902.06679/) (2019)
- [MODEL: Motif-based Deep Feature Learning for Link Prediction](https://mlsanity.com/paper/2008.03637/) (2020)
- [Link Prediction in Complex Networks: A Mutual Information Perspective](https://mlsanity.com/paper/1405.4341/) (2014)
- [Common neighbours and the local-community-paradigm for link prediction in bipartite networks](https://mlsanity.com/paper/1504.07011/) (2015)
- [Experimental analyses on 2-hop-based and 3-hop-based link prediction algorithms](https://mlsanity.com/paper/1909.00174/) (2019)

## Abstract (arXiv)

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.