Graph kernel of brain networks considering functional similarity measures.

As a tool of brain network analysis, the graph kernel is often used to assist the diagnosis of neurodegenerative diseases. It is used to judge whether the subject is sick by measuring the similarity between brain networks. Most of the existing graph kernels calculate the similarity of brain networks based on structural similarity, which can better capture the topology of brain networks, but all ignore the functional information including the lobe, centers, left and right brain to which the brain region belongs and functions of brain regions in brain networks. The functional similarities can help more accurately locate the specific brain regions affected by diseases so that we can focus on measuring the similarity of brain networks. Therefore, a multi-attribute graph kernel for the brain network is proposed, which assigns multiple attributes to nodes in the brain network, and computes the graph kernel of the brain network according to Weisfeiler-Lehman color refinement algorithm. In addition, in order to capture the interaction between multiple brain regions, a multi-attribute hypergraph kernel is proposed, which takes into account the functional and structural similarities as well as the higher-order correlation between the nodes of the brain network. Finally, the experiments are conducted on real data sets and the experimental results show that the proposed methods can significantly improve the performance of neurodegenerative disease diagnosis. Besides, the statistical test shows that the proposed methods are significantly different from compared methods.

BIC-LP: A Hybrid Higher-Order Dynamic Bayesian Network Score Function for Gene Regulatory Network Reconstruction.

Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ScoreLasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.

Aestuarium zhoushanense is a later heterotypic synonym of Marivivens donghaensis, and transfer of Paradonghicola geojensis to the genus Marivivens as Marivivens geojensis comb. nov.

The 16S rRNA genes of Aestuarium zhoushanense G7T and Paradonghicola geojensis FJ12T shared 100 % sequence identity with Marivivens donghaensis AM-4T. Phylogeny of 16S rRNA gene sequences showed that the three type strains formed a monophyletic clade within the genus Marivivens. Whole genome sequence comparisons showed that three type strains shared 46.7-69.7 % digital DNA-DNA hybridization, 92.1-96.4 % average nucleotide identity and 96.2-98.1 % average amino acid identity. The high 16S rRNA gene similarity values show that three type strains should belong to the same genus. The pan-genome of the five strains contained 5754 genes including 1877 core genes. Based on the principle of priority, we propose that A. zhoushanense Yu et al. 2019 is a later heterotypic synonym of M. donghaensis Park et al. 2016, and P. geojensis should be reclassified as Marivivens geojensis comb. nov., respectively.

The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer.

BACKGROUND: The occurrence and development of breast cancer has a strong correlation with a person's genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level. METHODS: In this study, we complete an analysis of the relevant protein-protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein-protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis. RESULTS: The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein-protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes. CONCLUSIONS: These genes could provide a valuable reference in clinical treatment among breast cancer patients.