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Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.

In order to apply Radio Frequency Micro-nano-Electro-Mechanical System (MEMS/NEMS) technologies to produce miniature, high isolation, low insertion loss, good linear characteristic, and low power consumption microwave switches, we present a novel NEMS switch with nanoscaling in this paper through the analysis of electrics and mechanics of the RF switch. The measured data show the pull-in voltage of 24.1 V and the good RF performance of the insertion loss of below −10 dB at 0 GHz on the “on” state, and the isolation of beyond –40 dB at 0–40 GHz on the “off” state, indicating that the witch is suitable for the 0–40 GHz applications. Our analysis shows that the NEMS switch not only can work in wide frequency bands, but also has better isolation performance in lower frequency, thus extending the application to the lower band. The Haar-wavelet-based multiresolution time domain (MRTD) with compactly supported scaling function is used for modeling and analyzing the nanomachine switch for the first time. The major advantage of the MRTD algorithms is their capability to develop real-time time and space adaptive grids through the efficient thresholding of the wavelet coefficients. The error between the measured and computed results is below 5%, this indicated that the Haar-wavelet-based multiresolution time domain was suitable for simulating the nano-scaling contact switch.

Background: Complex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affect the spine and the sacroiliac joints. The pathogenesis of SpA remains largely unknown. Results: In this paper, we conducted a network study of the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a SpA specific PPI network, identified potential pathways associated with SpA, and finally sketched an overview of biological processes involved in the development of SpA. Conclusions: The protein-protein interaction (PPI) network and pathways reflect the link between the two pathological processes of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modelling caused new boneformation and bone loss. We found that some known disease causative genes, such as TNFand ILs, play pivotal roles in this interaction. [ABSTRACT FROM AUTHOR]