MOTIVATION: Machine learning algorithms excavate important variables from big data. However, deciding on the relevance of identified variables is challenging. The addition of artificial noise, 'decoy' variables, to raw data, 'target' variables, enables calculating a false-positive rate and a biological relevance probability for each variable rank. These scores allow the setting of a cut-off for informative variables, depending on the required sensitivity/specificity of a scientific question. RESULTS: We tested the function of the Target-Decoy MineR (TDM) using synthetic data with different degrees of perturbation. Following, we applied the TDM to experimental Omics (metabolomics, transcriptomics and proteomics) results. The TDM graphs indicate the degree of difference between sample groups. Further, the TDM reports the contribution of each variable to correct classification, i.e. its biological relevance. AVAILABILITYAND IMPLEMENTATION: An implementation of the algorithm in R is freely available from https://bitbucket.org/cesaremov/targetdecoy_mining/. The Target-Decoy MineR is applicable to different types of quantitative data in tabular format. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Service-oriented architectures (SoAs) are a popular paradigm for enterprise and data center computing but normally do not perform well on tactical networks, which are often degraded in terms of bandwidth, reliability, latency, and connectivity. This paper presents the agile computing middleware and in particular a transparent network proxy and associated protocols that help address the impedance mismatch that occurs between SoAs and tactical and DIL (Disconnected, Intermittent, and Limited) networks.

This work contributes to the numerical solution of the inverse problem of determining an isotropic conductivity from boundary measurements, known as electrical impedance tomography. To this end, we first investigate the imaging resolution of the complete electrode model in a circular geometry using analytic solutions of the forward problem and conformal maps. Based on this information we propose a novel discretization of the conductivity space which explicitly depends on the electrode sizes and locations. Roughly speaking, the resulting conductivity meshes comply with the maximal resolution provided by discrete data with a known noise level. We heuristically extend this approach to domains of arbitrary shape and present its performance under a Newton-type inversion algorithm.

Studies show that about 50% of web search is for information exploration purpose, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potentially large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. With meta information embedded, keyword search on structured data provides the potential for automating or semi-automating the comparison of multiple results. In this demo we present a system XSACT for differentiating search results on structured data. XSACT takes as input a set of structured results, and outputs a Differentiation Feature Set (DFS) for each result to highlight their differences within a size bound. The problem of generating DFSs with maximal differences is proved to be NP-hard. XSACT adopts efficient algorithms for DFS generation, and features a user-friendly interface that effectively interacts with the users to help them compare search results.