IEEE/ACM Trans Comput Biol Bioinform. 2020 Sep-Oct;17(5):1595-1604. doi: 10.1109/TCBB.2019.2907626. Epub 2019 Mar 26.
Many current studies have evidenced that microbes play important roles in human diseases. Therefore, discovering the associations between microbes and diseases is beneficial to systematically understanding the mechanisms of diseases, diagnosing, and treating complex diseases. It is well known that finding new potential microbe-disease associations via biological experiments is a time-consuming and expensive process. However, the computation methods can provide an opportunity to effectively predict microbe-disease associations. In recent years, efforts toward predicting microbe-disease associations are not in proportional to the importance of microbes to human diseases. In this study, we develop a method (called BRWMDA) to predict new microbe-disease associations based on similarity and improving bi-random walk on the disease and microbe networks. BRWMDA integrates microbe network, disease network, and known microbe-disease associations into a single network. After calculating the Gaussian Interaction Profile (GIP) kernel similarity of microbes based on known microbe-disease associations, the microbe network is obtained by adjusting the similarity with the logistics function. In addition, the disease network is computed by the similarity network fusion (SNF) method with the symptom-based similarity and the GIP kernel similarity based on known microbe-disease associations. Then, these two networks of microbe and disease are connected by known microbe-disease associations. Based on the assumption that similar microbes are normally associated with similar diseases and vice versa, BRWMDA is employed to predict new potential microbe-disease associations via random walk with different steps on microbe and disease networks, which reasonably uses the similarity of microbe network and disease network. The 5-fold cross validation and Leave One Out Cross Validation (LOOCV) are adopted to assess the prediction performance of our BRWMDA algorithm, as well as other competing methods for comparison. 5-fold cross validation experiments show that BRWMDA obtained the maximum AUC value of 0.9087, which is again superior to other methods of 0.9025(NGRHMDA), 0.8797 (LRLSHMDA), 0.8571 (KATZHMDA), 0.7782 (HGBI), and 0.5629 (NBI). In addition, BRWMDA also outperforms other methods in terms of LOOCV, whose AUC value is 0.9397, which is superior to other methods of 0.9111(NGRHMDA), 0.8909 (LRLSHMDA), 0.8644 (KATZHMDA), 0.7866 (HGBI), and 0.5553 (NBI). Case studies also illustrate that BRWMDA is an effective method to predict microbe-disease associations.
许多当前的研究表明，微生物在人类疾病中起着重要作用。因此，发现微生物与疾病之间的关联有助于系统地了解疾病的机制，进行诊断和治疗复杂疾病。众所周知，通过生物实验发现新的潜在微生物-疾病关联是一个耗时且昂贵的过程。然而，计算方法可以提供有效预测微生物-疾病关联的机会。近年来，寻找微生物-疾病关联的努力与微生物对人类疾病的重要性不成比例。在这项研究中，我们开发了一种方法（称为 BRWMDA），该方法基于疾病和微生物网络上的相似性和改进的双随机游走来预测新的微生物-疾病关联。BRWMDA 将微生物网络、疾病网络和已知的微生物-疾病关联整合到一个单一的网络中。基于已知的微生物-疾病关联，根据微生物的高斯相互作用分布（GIP）核相似性计算微生物的相似性，然后通过物流函数调整相似性得到微生物网络。此外，通过基于症状的相似性和基于已知微生物-疾病关联的 GIP 核相似性的相似网络融合（SNF）方法计算疾病网络。然后，通过已知的微生物-疾病关联将这两个微生物和疾病网络连接起来。基于相似的微生物通常与相似的疾病相关，反之亦然的假设，BRWMDA 通过在微生物和疾病网络上进行不同步长的随机游走来预测新的潜在微生物-疾病关联，合理利用了微生物网络和疾病网络的相似性。我们采用 5 折交叉验证和留一法交叉验证（LOOCV）来评估我们的 BRWMDA 算法以及其他竞争方法的预测性能。5 折交叉验证实验表明，BRWMDA 获得的最大 AUC 值为 0.9087，再次优于其他方法的 0.9025（NGRHMDA）、0.8797（LRLSHMDA）、0.8571（KATZHMDA）、0.7782（HGBI）和 0.5629（NBI）。此外，BRWMDA 在 LOOCV 方面也优于其他方法，其 AUC 值为 0.9397，优于其他方法的 0.9111（NGRHMDA）、0.8909（LRLSHMDA）、0.8644（KATZHMDA）、0.7866（HGBI）和 0.5553（NBI）。案例研究也表明，BRWMDA 是一种预测微生物-疾病关联的有效方法。
