PBHMDA: Path-Based Human Microbe-Disease Association Prediction

Abstract

With the advance of sequencing technology and microbiology, the microorganisms have been found to be closely related to various important human diseases. The increasing identification of human microbe-disease associations offers important insights into the underlying disease mechanism understanding from the perspective of human microbes, which are greatly helpful for investigating pathogenesis, promoting early diagnosis and improving precision medicine. However, the current knowledge in this domain is still limited and far from complete. Here, we present the computational model of Path-Based Human Microbe-Disease Association prediction (PBHMDA) based on the integration of known microbe-disease associations and the Gaussian interaction profile kernel similarity for microbes and diseases. A special depth-first search algorithm was implemented to traverse all possible paths between microbes and diseases for inferring the most possible disease-related microbes. As a result, PBHMDA obtained a reliable prediction performance with AUCs (The area under ROC curve) of 0.9169 and 0.8767 in the frameworks of both global and local leave-one-out cross validations, respectively. Based on 5-fold cross validation, average AUCs of 0.9082 ± 0.0061 further demonstrated the efficiency of the proposed model. For the case studies of liver cirrhosis, type 1 diabetes, and asthma, 9, 7, and 9 out of predicted microbes in the top 10 have been confirmed by previously published experimental literatures, respectively. We have publicly released the prioritized microbe-disease associations, which may help to select the most potential pairs for further guiding the experimental confirmation. In conclusion, PBHMDA may have potential to boost the discovery of novel microbe-disease associations and aid future research efforts toward microbe involvement in human disease mechanism. The code and data of PBHMDA is freely available at http://www.escience.cn/system/file?fileId=85214.

RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction

“…As a result, 9 out of the top 10, 19 out of the top 20, and 47 out of the top 50 predicted Esophageal Neoplasms related miRNAs were confirmed based on dbDEMC and miR2Disease (1st column: top 1-25; 2nd column: top could be introduced to the prediction of miRNA-disease association. [55][56][57] Finally, we would further improve the current version of RKNNMDA to realize the miRNA-disease association types, 54 disease-related miRNA-target interactions, and disease-related miRNA-environmental factors. 58,59 A cancer hallmark network framework provides solutions to solve the mentioned limitations of RKNNMDA, which can effectively evaluate cancer risks based on miRNA profiles.…”

BRWSP: Predicting circRNA-Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network

“…In order to analyse the performance of the BRWSP algorithm in predicting circRNA-disease associations, BRWSP (L 3, q 0.12, maxiter 300, and α 1) is compared with KATZHCDA [33], iCircDA-MF [36], RLS-Kron [37,54], and DFSPW [38][39][40][41]. Herein, for DFSPW algorithm, it rst searches all paths between circRNAs and diseases and then calculates the score between circRNAs and diseases based on paths by 6 Complexity formula (4).…”

“…Herein, for DFSPW algorithm, it rst searches all paths between circRNAs and diseases and then calculates the score between circRNAs and diseases based on paths by 6 Complexity formula (4). For DFSPW algorithm's parameters, the maximum length of path and the decay factor are equal to 3 and 2.26, respectively, based on the previous study [38][39][40][41].…”

“…(1) Ba-Alawi et al [38] used depth-first search algorithm to traverse all simple paths between a specific drug and a specific target protein and then aggregated the score from these searched paths to infer drug-target interactions. en this algorithm was extended to identify miRNA-disease associations [39], lncRNAdisease associations [40], circRNA-disease associations [32], and microbe-disease associations [41] and obtained satisfactory performance. However, this algorithm needs to search for all paths between a specific circRNA and a specific disease.…”

“…ere are four parameters in the BRWSP algorithm. Among them, we set the path length L is equal to 3 based on the previous studies [38][39][40][41]. However, the values of q, maxiter, and decay factor α are unde ned.…”