Abstract：
Since the problem proposed in late 2000s, microRNA-disease association (MDA) predictions have been implemented based on the data fusion paradigm. Integrating diverse data sources gains a more comprehensive research perspective, and brings a challenge to algorithm design for generating accurate, concise and consistent representations of the fused data. After more than a decade of research progress, a relatively simple algorithm like the score function or a single computation layer may no longer be sufficient for further improving predictive performance. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. In the current review, we present 29 state-of-the-art models and introduce the taxonomy of computational models for MDA prediction based on model fusion and non-fusion. The new taxonomy exhibits notable changes in the algorithmic architecture of models, compared with that of earlier ones in the 2017 review by Chen et al. Moreover, we discuss the progresses that have been made towards overcoming the obstacles to effective MDA prediction since 2017 and elaborated on how future models can be designed according to a set of new schemas. Lastly, we analysed the strengths and weaknesses of each model category in the proposed taxonomy and proposed future research directions from diverse perspectives for enhancing model performance.
摘要：
自从2000年代末提出问题以来，microRNA-疾病关联(MDA)预测已经基于数据融合范例实现。整合不同的数据源可以获得更全面的研究视角，并给算法设计带来了挑战，融合数据的简洁和一致的表示。经过十多年的研究，相对简单的算法，如得分函数或单个计算层可能不再足以进一步提高预测性能。先进的模型设计近年来变得越来越频繁，特别是以合理梳理多种算法的形式，称为模型融合的过程。在当前的审查中，我们介绍了29个最新的模型，并介绍了基于模型融合和非融合的MDA预测计算模型的分类法。新的分类法在模型的算法架构中表现出显著的变化，与Chen等人2017年评论中的早期相比。此外，我们讨论了自2017年以来在克服有效MDA预测障碍方面取得的进展，并阐述了如何根据一组新模式来设计未来模型。最后,我们分析了拟议分类法中每个模型类别的优缺点，并从不同角度提出了未来的研究方向，以提高模型性能。
