A survey on multi-view fusion for predicting links in biomedical bipartite networks: Methods and applications

Biomedical research increasingly relies on the analysis of complex interactions between biological entities, such as genes, proteins, and drugs. Although advancements in biomedical technologies have led to a vast accumulation of relational data, the high cost and time demands of wet-lab experiments have limited the number of verified interactions. Thus, computational methods have become essential for predicting potential links by leveraging diverse datasets to efficiently and accurately identify promising interactions. Multi-view fusion, which combines complementary information from multiple sources, has shown significant promise for enhancing the prediction accuracy and robustness. We introduce the framework of multi-view fusion methods by elaborating on key components. This includes a comprehensive examination of multi-view data sources covering various omics and biological databases. We then describe the feature extraction techniques and explore how meaningful features can be derived from heterogeneous data formats. Next, we offer an in-depth review of the fusion strategies and categorize them as early fusion, late fusion, and fusion during the training phase. We discuss the advantages and limitations of each approach, emphasizing the need for sophisticated techniques that consider the unique attributes of biological link prediction. We also provide an overview of the commonly used datasets, evaluation metrics, and validation techniques. Commonly used datasets serve as reliable benchmarks for evaluating the computational models. Evaluation metrics and validation techniques are crucial for reliably assessing the performances of link prediction models. Subsequently, a comparative analysis of different fusion methods is conducted to empirically evaluate their performances on widely available biomedical datasets. This yielded valuable insights into the strengths and limitations of each approach in real-world applications. Finally, we identify key obstacles such as data heterogeneity, model robustness, and missing data and suggest potential directions for future research. Our findings offer valuable insights into the applications and future directions of multi-view fusion methods for biomedical link prediction, highlighting their potential to accelerate discovery and innovation in the field.