ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical Interactions Discovering

Biomedical information graphs are crucial for interaction discovering of biomedical information in modern age, such as identification of multifarious molecular interactions and drug discovery, which attracts increasing interests in biomedicine, bioinformatics, and human healthcare communities. Nowadays, more and more graph neural networks have been proposed to learn the entities of biomedical information and precisely reveal biomedical molecule interactions with state-of-the-art results. These methods remedy the fading of features from a far distance but suffer from remedying such problem at the expensive cost of redundant memory and time. In our paper, we propose a novel Residual Message Graph Convolution Network (ResMGCN) for fast and precise biomedical interaction prediction in a different idea. Specifically, instead of enhancing the message from far nodes, ResMGCN aggregates lower-order information with the next round higher information to guide the node update to obtain a more meaningful node representation. ResMGCN is able to perceive and preserve various messages from the previous layer and high-order information in the current layer with least memory and time cost to obtain informative representations of biomedical entities. We conduct experiments on four biomedical interaction network datasets, including protein-protein, drug-drug, drug-target, and gene-disease interactions, which demonstrates that ResMGCN outperforms previous state-of-the-art models while achieving superb effectiveness on both storage and time.Comment: In progres

Asymptotic Behavior of Random Defective Parking Functions

Suppose that m drivers each choose a preferred parking space in a linear car park with n spots. In order, each driver goes to their desired spot and parks there if possible. If the spot is already occupied then the car parks in the first available spot after that; if no such spot is available then the car leaves the street without parking. When m \u3e n, there will always be defects–cars that are not able to park. Building upon the work in Cameron et al. Counting defective parking functions, we introduce a multi-shuffle construction to defective parking functions and investigate parking statistics of a defective parking function chosen uniformly at random

Integrated analysis of mRNA-seq and miRNA-seq reveals the potential roles of sex-biased miRNA-mRNA pairs in gonad tissue of dark sleeper (Odontobutis potamophila)

Abstract Background The dark sleeper (Odontobutis potamophila) is an important commercial fish species which shows a sexually dimorphic growth pattern. However, the lack of sex transcriptomic data is hindering further research and genetically selective breeding of the dark sleeper. In this study, integrated analysis of mRNA and miRNA was performed on gonad tissue to elucidate the molecular mechanisms of sex determination and differentiation in the dark sleeper. Results A total of 143 differentially expressed miRNAs and 16,540 differentially expressed genes were identified. Of these, 8103 mRNAs and 75 miRNAs were upregulated in testes, and 8437 mRNAs and 68 miRNAs were upregulated in ovaries. Integrated analysis of miRNA and mRNA expression profiles predicted more than 50,000 miRNA-mRNA interaction sites, and among them 27,583 negative miRNA-mRNA interactions. A number of sex related genes were targeted by sex-biased miRNAs. The relationship between 15 sex-biased genes and 15 sex-biased miRNAs verified by using qRT-PCR were described. Additionally, a number of SNPs were revealed through the transcriptome data. Conclusions The overall results of this study facilitate our understanding of the molecular mechanism underlying sex determination and differentiation and provide valuable genomic information for selective breeding of the dark sleeper

Properties of frequentist confidence levels derivatives

In high energy physics, results from searches for new particles or rare processes are often reported using a modified frequentist approach, known as $\rm{CL_s}$ method. In this paper, we study the properties of the derivatives of $\rm{CL_s}$ and $\rm{CL_{s+b}}$ as signal strength estimators if the confidence levels are interpreted as credible intervals. Our approach allows obtaining best fit points and $\chi^2$ functions which can be used for phenomenology studies. In addition, this approach can be used to incorporate $\rm{CL_s}$ results into Bayesian combinations

Measurements of Δmd,s\Delta m_{d,s} and ΔΓd\Delta \Gamma_d at LHCb

List of know-miRNA and novel-miRNA of dark sleeper. (DOCX 57 kb

Additional file 9: Figure S3. of Integrated analysis of mRNA-seq and miRNA-seq reveals the potential roles of sex-biased miRNA-mRNA pairs in gonad tissue of dark sleeper (Odontobutis potamophila)

Length distribution of small RNA in six miRNA libraries. (DOCX 109 kb

Additional file 3: Table S3. of Integrated analysis of mRNA-seq and miRNA-seq reveals the potential roles of sex-biased miRNA-mRNA pairs in gonad tissue of dark sleeper (Odontobutis potamophila)

Summary of sequence data generated for dark sleeper transcriptome, and quality filtering. (DOCX 13 kb