School of Computer Science and Technology, Heilongjiang University, Harbin, China.
BMC Bioinformatics. 2022 Nov 14;23(1):483. doi: 10.1186/s12859-022-04961-y.
Microorganisms in the human body have a great impact on human health. Therefore, mastering the potential relationship between microorganisms and diseases is helpful to understand the pathogenesis of diseases and is of great significance to the prevention, diagnosis, and treatment of diseases. In order to predict the potential microbial disease relationship, we propose a new computational model. Firstly, a bi-directional heterogeneous microbial disease network is constructed by integrating multiple similarities, including Gaussian kernel similarity, microbial function similarity, disease semantic similarity, and disease symptom similarity. Secondly, the neighbor information of the network is learned by random walk; Finally, the selection model is used for information aggregation, and the microbial disease node pair is analyzed. Our method is superior to the existing methods in leave-one-out cross-validation and five-fold cross-validation. Moreover, in case studies of different diseases, our method was proven to be effective.
人体内的微生物对人类健康有很大的影响。因此，掌握微生物与疾病之间的潜在关系有助于了解疾病的发病机制，对疾病的预防、诊断和治疗具有重要意义。为了预测潜在的微生物疾病关系，我们提出了一种新的计算模型。首先，通过整合多种相似性，包括高斯核相似性、微生物功能相似性、疾病语义相似性和疾病症状相似性，构建了一个双向异质微生物疾病网络。其次，通过随机游走学习网络的邻居信息；最后，利用选择模型进行信息聚合，分析微生物疾病节点对。我们的方法在留一交叉验证和五折交叉验证中优于现有方法。此外，在不同疾病的案例研究中，我们的方法被证明是有效的。
