A Review of Link Prediction Applications in Network Biology

TL;DR

This review comprehensively analyzes link prediction methods in network biology, highlighting their applications, performance, challenges, and future directions in understanding complex biological interactions.

Contribution

It systematically dissects various LP approaches, evaluates their performance on biological datasets, and discusses future model characteristics for advancing biological network analysis.

Findings

LP methods effectively predict biological interactions.

Embedding-based approaches outperform traditional metrics.

LP models help address noise and data sparsity in biological networks.

Abstract

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.