For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ± 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA.
Vital node identification is crucial for understanding the topology of network structures as well as controlling the spreading process in complex systems. Even though many node ranking metrics have been designed for vital node identification in static networks, there is a lack of research in temporal systems. And how the temporal information influence node ranking is still unknown. In this work, we propose a temporal information gathering (TIG) process for temporal networks. TIG-process, as a node's importance metric, can be used to do the node ranking. As a framework, TIG-process can be applied to explore the impact of temporal information on nodes' significance. The key point of the TIG-process is that node's importance relies on the importance of its neighborhood. There are four parameters, including the temporal information gathering depth n, temporal distance matrix D, initial information c and weighting function f. We observe that TIG-process can degenerate to classic metrics both from static and temporal networks by proper combination of these four parameters. In addition, fastest arrival distance based TIG-process (fad-tig) performs much better in quantifying nodes' efficiency and nodes' spreading influence than the one based on temporal shortest distance. For the fad-tig process, we can find an optimal gathering depth n which makes the TIG-process perform the best when n is
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