Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina.
Brief Bioinform. 2022 Mar 10;23(2). doi: 10.1093/bib/bbac003.
The gene ontology (GO) provides a hierarchical structure with a controlled vocabulary composed of terms describing functions and localization of gene products. Recent works propose vector representations, also known as embeddings, of GO terms that capture meaningful information about them. Significant performance improvements have been observed when these representations are used on diverse downstream tasks, such as the measurement of semantic similarity between GO terms and functional similarity between proteins. Despite the success shown by these approaches, existing embeddings of GO terms still fail to capture crucial structural features of the GO. Here, we present anc2vec, a novel protocol based on neural networks for constructing vector representations of GO terms by preserving three important ontological features: its ontological uniqueness, ancestors hierarchy and sub-ontology membership. The advantages of using anc2vec are demonstrated by systematic experiments on diverse tasks: visualization, sub-ontology prediction, inference of structurally related terms, retrieval of terms from aggregated embeddings, and prediction of protein-protein interactions. In these tasks, experimental results show that the performance of anc2vec representations is better than those of recent approaches. This demonstrates that higher performances on diverse tasks can be achieved by embeddings when the structure of the GO is better represented. Full source code and data are available at https://github.com/sinc-lab/anc2vec.
基因本体论（GO）提供了一个层次结构，其中包含一个由术语组成的受控词汇表，这些术语描述了基因产物的功能和定位。最近的研究提出了 GO 术语的向量表示形式，也称为嵌入，表示，这些表示形式捕捉了有关它们的有意义的信息。在使用这些表示形式进行各种下游任务（例如，测量 GO 术语之间的语义相似性和蛋白质之间的功能相似性）时，已经观察到了显著的性能改进。尽管这些方法取得了成功，但现有的 GO 术语嵌入仍然无法捕捉 GO 的关键结构特征。在这里，我们提出了 anc2vec，这是一种基于神经网络的新协议，通过保留三个重要的本体论特征来构建 GO 术语的向量表示：其本体论独特性、祖先层次结构和子本体成员资格。anc2vec 的优势通过在各种任务上的系统实验得到了证明：可视化、子本体预测、结构相关术语的推断、从聚合嵌入中检索术语以及蛋白质-蛋白质相互作用的预测。在这些任务中，实验结果表明 anc2vec 表示的性能优于最近的方法。这表明，通过更好地表示 GO 的结构，可以在各种任务上实现更高的性能。完整的源代码和数据可在 https://github.com/sinc-lab/anc2vec 上获得。
