miRDB: An online database for prediction of functional microRNA targets

MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org

High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Models

We consider the problem of jointly estimating multiple related directed acyclic graph (DAG) models based on high-dimensional data from each graph. This problem is motivated by the task of learning gene regulatory networks based on gene expression data from different tissues, developmental stages or disease states. We prove that under certain regularity conditions, the proposed $\ell_0$-penalized maximum likelihood estimator converges in Frobenius norm to the adjacency matrices consistent with the data-generating distributions and has the correct sparsity. In particular, we show that this joint estimation procedure leads to a faster convergence rate than estimating each DAG model separately. As a corollary, we also obtain high-dimensional consistency results for causal inference from a mix of observational and interventional data. For practical purposes, we propose \emph{jointGES} consisting of Greedy Equivalence Search (GES) to estimate the union of all DAG models followed by variable selection using lasso to obtain the different DAGs, and we analyze its consistency guarantees. The proposed method is illustrated through an analysis of simulated data as well as epithelial ovarian cancer gene expression data

Energy-Efficient User Access Control and Resource Allocation in HCNs with Non-Ideal Circuitry

In this paper, we study the energy-efficient user access control (UAC) based on resource allocation (RA) in heterogeneous cellular networks (HCNs) with the required downlink data rate under non-ideal power amplifiers (PAs) and circuit power. It is proved that the energy consumption minimization is achieved when the typical user accesses only one base station (BS), while the other BSs remain in idle mode on the transmission resource allocated to this user. For this purpose, we reformulate the original non-convex optimization problem into a series of convex optimization problems where, in each case, the transmit power and duration of the accessed BS are determined. Then, the BS with the minimal energy consumption is selected for transmission. Considering the approximate situation, it is showed that the optimal transmit duration of the accessed BS can be estimated in closed form. The benefits of our proposed UAC and RA schemes are validated using numerical simulations, which also characterize the effect that non-ideal PAs have on the total energy consumption of different transmission schemes.Comment: 6 pages, 4 figures, 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP

Optimal Pilot Symbols Ratio in terms of Spectrum and Energy Efficiency in Uplink CoMP Networks

In wireless networks, Spectrum Efficiency (SE) and Energy Efficiency (EE) can be affected by the channel estimation that needs to be well designed in practice. In this paper, considering channel estimation error and non-ideal backhaul links, we optimize the pilot symbols ratio in terms of SE and EE in uplink Coordinated Multi-point (CoMP) networks. Modeling the channel estimation error, we formulate the SE and EE maximization problems by analyzing the system capacity with imperfect channel estimation. The maximal system capacity in SE optimization and the minimal transmit power in EE optimization, which both have the closed-form expressions, are derived by some reasonable approximations to reduce the complexity of solving complicated equations. Simulations are carried out to validate the superiority of our scheme, verify the accuracy of our approximation, and show the effect of pilot symbols ratio.Comment: 5 pages, 3 figures, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring

Development of x-ray holography methods for structure determination : Application of high speed detectors and novel numerical methods

Holographic methods show much promise to enable direct determination of atomic structure with minimal assumptions and approximations. The approach can, in principle, provide three dimensional information on atomic positions. However, significant developments in experimental techniques, instrumentation and in data collection and analysis are needed. A review of the holography method is given with a focus on X-ray fluorescence holography. Methods for analysis of X-ray holographic data are also reviewed. An overview of the detectors relevant to X-ray measurements is also presented. An experimental apparatus for rapid acquisition of X-ray holographs using novel X-ray detectors has been developed. The integration of high speed detectors and the utilization of rapid sampling methods to produce high quality holograms form the core of this work. A new method for direct extraction of the electron charge density based on expansion of the hologram with respect to a spherical harmonic basis is developed. This approach attacks the problem of obtaining the electron density from the hologram by the introduction of periodic constraints (fixed unit cells) while maintaining flexibility by making no assumptions about the positions of atoms within the unit cells. Problems with local or long range distortions can be solved by utilizing cells of the appropriate size. The method makes no other assumptions. Model charge densities derived from this approach are shown to match quite well with the input model crystal structures with no need for heavy filtering typical of the Barton Transform. The algorithm can be fully automated and hence falls into the class of Direct Methods . This new approach may move the method of X-ray holography from the developmental stage to a powerful and routine tool for the solution of single crystal structures relevant to inorganic materials and organic systems

Direct Estimation of Differences in Causal Graphs

We consider the problem of estimating the differences between two causal directed acyclic graph (DAG) models with a shared topological order given i.i.d. samples from each model. This is of interest for example in genomics, where changes in the structure or edge weights of the underlying causal graphs reflect alterations in the gene regulatory networks. We here provide the first provably consistent method for directly estimating the differences in a pair of causal DAGs without separately learning two possibly large and dense DAG models and computing their difference. Our two-step algorithm first uses invariance tests between regression coefficients of the two data sets to estimate the skeleton of the difference graph and then orients some of the edges using invariance tests between regression residual variances. We demonstrate the properties of our method through a simulation study and apply it to the analysis of gene expression data from ovarian cancer and during T-cell activation