  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.
