Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

The Relationship between Foreign Direct Investment, Current Account and Economic Growth in Vietnam: A Framework for International Capital Flow Management

The relationship between foreign direct investment (FDI), current account, and economic growth is a subject that always attracts the attention of researchers with studies that focus on both developed and developing countries. Many studies have shown that FDI has a positive effect on the growth of countries. However, this capital also brings some risks. Therefore, this study evaluates the relationship between foreign direct investment, current account, and economic growth in Vietnam. Using the VECM method combined with the Bayesian stability test, the research results have shown that, in both the short and long term, FDI and current accounts positively affect Vietnam’s economic growth. Based on research results, we propose policy implications to minimize the negative effects of FDI inflows and make the most of this capital source for the sustainable economic development of Vietnam

The Impact of Supporting Industries on Attracting Foreign Direct Investment: A Case Study in Vinh Phuc Province, Vietnam

 Summary: This study focuses on explaining the theoretical basis of the impact of supporting industries (SI) on attracting foreign direct investment (FDI); assessing the state of the impact of SI on FDI attraction into Vietnam in general and Vinh Phuc province in particular. Quantitative analysis results show that, in the field of SI in Vietnam, import suppliers are dominating domestic suppliers, the factor that most affects FDI enterprises' satisfaction level is labor, especially hard-work and progressiveness, followed by quality and attitude of discipline compliance of labor resources. The domestic SI still face difficulties in approaching customers, quality assurance, with outdated technology, lack of high-tech manpower, poor innovative research capabilities... In the coming time, to contribute to FDI attraction into Vietnam, to become a supplier for FDI enterprises, domestic enterprises working in supporting industries (SI enterprises) need to increase the rate of capital investment on technology, improve the quality of human resources, and promote information exchange with FDI enterprises

EfficientRec an unlimited user-item scale recommendation system based on clustering and users interaction embedding profile

Recommendation systems are highly interested in technology companies nowadays. The businesses are constantly growing users and products, causing the number of users and items to continuously increase over time, to very large numbers. Traditional recommendation algorithms with complexity dependent on the number of users and items make them difficult to adapt to the industrial environment. In this paper, we introduce a new method applying graph neural networks with a contrastive learning framework in extracting user preferences. We incorporate a soft clustering architecture that significantly reduces the computational cost of the inference process. Experiments show that the model is able to learn user preferences with low computational cost in both training and prediction phases. At the same time, the model gives a very good accuracy. We call this architecture EfficientRec with the implication of model compactness and the ability to scale to unlimited users and products.Comment: Published in 14th Asian Conference on Intelligent Information and Database Systems (ACIIDS), 202

A Regularization of the Backward Problem for Nonlinear Parabolic Equation with Time-Dependent Coefficient

We study the backward problem with time-dependent coefficient which is a severely ill-posed problem. We regularize this problem by combining quasi-boundary value method and quasi-reversibility method and then obtain sharp error estimate between the exact solution and the regularized solution. A numerical experiment is given in order to illustrate our results

Projekt Rocky - En analys av livscykelkostnader kring broräcken

Inom vägsektorn utgörs beslutsunderlaget för val av investeringsalternativ av investeringskostnaderna. Dock behöver inte en låg investeringskostnad innebära en låg totalkostnad då det är andra kostnadsfaktorer som också väger in i totalkostnaden. I examensarbetet undersöks livscykelkostnader, LCC, för för olika räckestyper för att kunna jämföra den ekonomiska lönsamheten hos dem

Curie Temperature of Diluted Magnetic Semiconductors: the Influence of the Antiferromagnetic Exchange Interaction

The coherent potential approximation and mean field approximation are used to calculate the free energy of the coupled carrier – localized spin system in III-V diluted magnetic semiconductors. Thus the magnetic transition temperature Tc can be determined and its dependence on important model parameters. We show that the strong antiferromagnetic superexchange interaction between nearest neighbour sites considerably reduces the Curie temperature