Info2vec: an aggregative representation method in multi-layer and heterogeneous networks

Mapping nodes in multi-layer and heterogeneous networks to low-dimensional vectors has wide applications in community detection, node classification and link prediction, etc. In this paper, a generalized graph representation learning framework is proposed for information aggregation in various multi-layer and heterogeneous networks. Specifically, an aggregation network is firstly obtained by graph transformation, generating potential information links based on the network structure on different layers. A comprehensive measurement of the similarity between different nodes in the aggregation network is then carried out by aggregating the information of nodes’ identities of structure, nearness and attributes etc. Based on the comprehensive similarity values the nodes have, a context graph can be generated using a simple edge percolation method, which provides a basis facilitating some important downstream work such as classification, clustering and prediction etc. We demonstrate the effectiveness of the new framework in identifying subnetworks in a cyberspace network, where it significantly outperforms all the existing baselines.Ministry of Education (MOE)G.Y. and Y.K. were supported by NSSFC 2019-SKJJ-C-005. G.X. was supported by the Ministry of Education (MOE), Singapore, under contract RG19/20

Computational Estimation and Experimental Verification of Off-Target Silencing during Posttranscriptional Gene Silencing in Plants

Successful application of posttranscriptional gene silencing (PTGS) for gene function study in both plants and animals depends on high target specificity and silencing efficiency. By computational analysis with genome and/or transcriptome sequences of 25 plant species, we predicted that about 50% to 70% of gene transcripts in plants have potential off-targets when used for PTGS that could obscure experimental results. We have developed a publicly available Web-based computational tool called siRNA Scan to identify potential off-targets during PTGS. Some of the potential off-targets obtained from this tool were tested by measuring the amount of off-target transcripts using quantitative reverse transcription-PCR. Up to 50% of the predicted off-target genes tested in plants were actually silenced when tested experimentally. Our results suggest that a high risk of off-target gene silencing exists during PTGS in plants. Our siRNA Scan tool is useful to design better constructs for PTGS by minimizing off-target gene silencing in both plants and animals