Traditional computational techniques are recently being improved with the use of prior biological knowledge from open-access repositories in the area of gene expression data analysis. In this work, we propose the use of prior knowledge as heuristic in an inference method of gene-gene associations from gene expression profiles. In this paper, we use Gene Ontology, which is an open-access ontology where genes are annotated using their biological functionality, as a source of prior knowledge together with a gene pairwise Gene-Ontology-based measure. The performance of our proposal has been compared to other benchmark methods for the inference of gene networks, outperforming in some cases and obtaining similar and competitive results in others, but with the advantage of providing simple and interpretable models, which is a desired feature for the Artificial Intelligence Health related models as stated by the European Union.

最近，通过使用来自基因表达数据分析领域开放存取存储库中的现有生物知识，对传统的计算技术进行了改进。在这项工作中，我们建议使用先验知识作为启发式方法，从基因表达谱中推断出基因-基因关联。在本文中，我们将基因本体（一种基于基因的生物学功能注释基因的开放存取本体）与基于基因对的基于基因本体的度量一起用作先验知识的来源。我们的提案的性能已与其他基准方法进行了基因网络推断，在某些情况下表现出色，在其他情况下却获得了类似的竞争结果，但其优势在于提供了简单易懂的模型，