Osteoarthritis (OA) significantly compromises the life quality of affected individuals and imposes a substantial economic burden on our society. Unfortunately the pathogenesis of the disease is till ...poorly understood and no effective medications have been developed. OA is a complex disease that involves both genetic and environmental influences. To elucidate the complex interlinked structure of metabolic processes associated with OA, we developed a differential correlation network approach to detecting the interconnection of metabolite pairs whose relationships are significantly altered due to the diseased process. Through topological analysis of such a differential network, we identified key metabolites that played an important role in governing the connectivity and information flow of the network. Identification of these key metabolites suggests the association of their underlying cellular processes with OA and may help elucidate the pathogenesis of the disease and the development of novel targeted therapies.
Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step ...process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.
Current studies of phenotype diversity by genome-wide association studies (GWAS) are mainly focused on identifying genetic variants that influence level changes of individual traits without ...considering additional alterations at the system-level. However, in addition to level alterations of single phenotypes, differences in association between phenotype levels are observed across different physiological states. Such differences in molecular correlations between states can potentially reveal information about the system state beyond that reported by changes in mean levels alone. In this study, we describe a novel methodological approach, which we refer to as genome metabolome integrated network analysis (GEMINi) consisting of a combination of correlation network analysis and genome-wide correlation study. The proposed methodology exploits differences in molecular associations to uncover genetic variants involved in phenotype variation. We test the performance of the GEMINi approach in a simulation study and illustrate its use in the context of obesity and detailed quantitative metabolomics data on systemic metabolism. Application of GEMINi revealed a set of metabolic associations which differ between normal and obese individuals. While no significant associations were found between genetic variants and body mass index using a standard GWAS approach, further investigation of the identified differences in metabolic association revealed a number of loci, several of which have been previously implicated with obesity-related processes. This study highlights the advantage of using molecular associations as an alternative phenotype when studying the genetic basis of complex traits and diseases.
In this paper there is presented and discussed a general analysis method for noise characterization of noisy multielement multiport differential networks. It is based on mixed mode, differential and ...common mode, noise waves representation of noise, generalized mixed-mode scattering parameters and generalized mixed-mode noise wave correlation parameters for the network. There are derived analytical relation between the noise figure for a given output port and the noise matrix and the scattering parameters of the network, as well as the correlations between the input port noise waves. The signal to noise ratio degradation factor is derived and discussed, too. Presented results can be implemented directly in a CAD software for noise analysis of differential microwave multi-element multiport networks with differential as well as with conventional single ended ports.
Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory ...programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.
In this paper there is presented and discussed a general analysis method for noise characterization of noisy multiport differential networks. It is based on mixed mode, differential and common mode, ...noise waves representation of noise, generalized mixed-mode scattering parameters and generalized mixed-mode noise wave correlation parameters for the network. There are derived analytical relation between the noise figure for a given output port and the noise matrix and the scattering parameters of the network, as well as the correlations between the input port noise waves. The signal to noise ratio degradation factor is derived and discussed, too. Presented results can be implemented directly in a CAD software noise analysis of differential microwave multiport networks with differential as well as with conventional single ended ports.
A design of a differentially exited directional coupler with the utilization of balanced lines is shown. An edge-coupled stripline technology is proposed for its physical realization. Such a ...realization technique is for the first time proposed for the design of multisection asymmetric couplers. For experimental verification a coupler operating between 0.29 and 1.7 GHz has been manufactured and measured.
Recently, the Erdafitinib was approved by the U.S. Food and Drug Administration, which is the first targeted therapy drug for genetic alteration in metastatic urothelial carcinoma. Cancer genomics ...research is greatly encouraged. Currently, a great number of gene regulatory networks between different states have been constructed, which can reveal the difference of biological states. However, they have not yet been applied to the subtypes of bladder cancer. Furthermore, gene regulatory networks are not constructed on their pathway, which provide insights for multi-target gene therapy. In this paper, we proposed a new method that construct gene regulatory networks under different conditions. Specifically, gene regulatory networks under different molecular subtypes of bladder cancer based on pathway are constructed. Besides, the regulatory differences between molecular subtypes are analyzed. We find that many hub genes in differential networks are related to cancer or even bladder cancer and these genes are significant diversity in subtypes of bladder cancer. In addition, attention should be paid to the links between hub genes and others, especially when the hub genes are the (candidate) target of drugs.
The vast majority of primary renal tumors were malignant and had a greater damage to the kidneys. The cause and development of tumor diseases were not only related to a single gene, but also caused ...by molecular aberrations in a network of complex genes or their generations. In this paper, we used differential network analysis method. The gene chip data of renal tumor related gene from Affymetrix hgu13b was firstly screened to obtain differential expressed genes, and the gene network was constructed separately under disease samples and normal samples. Secondly, the difference between disease network and normal genes was constructed, and the renal tumor related gene was analyzed. In our differential network model, we used a more accurate method than before. What's more, we used Bayesian information criteria to adjust the parameters, and the gene network under two different conditions was coded as the difference between the precision covariance matrices. More than two hundred pairs of gene pairs were identified from the differential network. Based on the differential network analysis of the central gene, GO enrichment analysis showed that the gene TBX3 had the highest degree of enrichment, indicating that it may be related to the formation of renal tumors. In the KEGG pathway enrichment analysis, it was found that p<0.05 was mainly enriched as a chemokine pathway, and chemokines had an important influence on the occurrence, metastasis and treatment of renal tumors.
A design of a differentially exited directional filter has been investigated theoretically and experimentally. In the filter edge-coupled symmetrical differentially-fed coupled-line sections have ...been utilized. The results of calculations show the proper operation of the designed filter. The experimental results of a second-order filter having center frequency equal 2.45 GHz are presented and commented.
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