MicroRNAs (miRNAs) are a class of short non-coding single-stranded RNA molecules that play a key role in gene expression regulation. Understanding the association between miRNAs and diseases is crucial for disease diagnosis and treatment. Although the wet experimental methods can be used to determine the associations, they are both laborious and expensive. In this paper, we propose a novel computational method called TWMHGT for predicting the associations between miRNAs and diseases based a two-way Multi-layer Heterogeneous Graph Transformer (MHGT) framework. For the first way, multi-view similarity of miRNAs and diseases is used as the input encodings for MHGT, and for the second way, random auto-encoders is used to generate the input encodings. In each MHGT way, the encodings of each layer are concatenated to obtain comprehensive embeddings of miRNAs and diseases, thereby integrating multiple high-level information. Then, the attention mechanisms are used to fuse the embeddings generated from the two-way MHGT. During the decoding stage, the fused embeddings are decoded to obtain the predicted association matrix by using matrix multiplication. Our model was benchmarked on two datasets and the 5-fold and 10-fold cross-validation results show that TWMHGT outperforms the state-of-the-art methods in terms of AUC, AUPR, accuracy, sensitivity, and specificity. Furthermore, we conducted case studies on three different diseases to validate the predictive performance of TWMHGT. The results show excellent performance in all cases, indicating the potential of TWMHGT in discovering novel miRNA-disease associations.

Reliable Software-Defined Networking (SDN) should mitigate forwarding anomalies due to cross-plane rule inconsistencies. Most existing countermeasures either inject probe packets to infer forwarding correctness or collect packet traces to detect forwarding anomalies. They, however, cannot detect or filter forwarding anomalies for production packets in real time. In this paper, we propose RuleOut as the first attempt to automatically throttle SDN forwarding anomalies. It disambiguates dependent rules via augmenting their matching fields with unique tags. Leveraging source routing, we further bind each packet with the tag sequence corresponding to rules the packet should match. RuleOut thus renders each packet to match at most one rule on each switch. This completely addresses the root cause of forwarding ambiguity. To implement RuleOut, we develop a non-overlapping rule dependency graph, a series of algorithms for incremental rule update and tag generation upon it, and various optimization techniques toward scalability and efficiency. We prototype RuleOut on the Ryu controller and Open vSwitch and evaluate its performance over public rule sets such as Stanford, Internet2, and Airtel1. RuleOut can use tags of only several bits long to disambiguate thousands to millions of rules and generate tags fairly fast within a few milliseconds.