Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput "omics" data typically available. To overcome this challenge, often referred to as the "small n, large p problem," we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE-Structure Learning for Hierarchical Networks-a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple Markov networks from high-dimensional data at large p/n ratios not previously feasible. We evaluated SHINE on Pan-Cancer data comprising 23 tumor types, and found that learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.

SUMMARY: Geneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases. AVAILABILITY AND IMPLEMENTATION: The most recent version of the package is available at https://github.com/montilab/hypeR.

Smart factory management is going through a remarkable change, in terms of quality and diversity of services provided to customers. The companies that produce manufacturing machines now can follow the products throughout the production chain, from the project to the deployment in real scenarios. Industry 4.0 is pushing this trend forward, demanding for servitization of products and machines, mainly for the manufacturing sector where human and production machine are in strict collaboration. The data produced by the machines must be processed quickly to allow the implementation of reactive services such as predictive maintenance and remote control, always taking care of the safety of nearby people. This paper proposes a multilayer architecture to tackle the main issues in monitoring legacy manufacturing machines and to provide general guidelines to solve them. We derived some guidelines from a real Industry 4.0 transition experiment performed together with the company technical departments to accomplish an efficient system for monitoring and servitization of manufacturing machines, with a scalable platform that confirms its usefulness in many production facilities with different needs.

MOTIVATION: Over the last decade, more diverse populations have been included in genome-wide association studies. If a genetic variant has a varying effect on a phenotype in different populations, genome-wide association studies applied to a dataset as a whole may not pinpoint such differences. It is especially important to be able to identify population-specific effects of genetic variants in studies that would eventually lead to development of diagnostic tests or drug discovery. RESULTS: In this paper, we propose PopCluster: an algorithm to automatically discover subsets of individuals in which the genetic effects of a variant are statistically different. PopCluster provides a simple framework to directly analyze genotype data without prior knowledge of subjects' ethnicities. PopCluster combines logistic regression modeling, principal component analysis, hierarchical clustering and a recursive bottom-up tree parsing procedure. The evaluation of PopCluster suggests that the algorithm has a stable low false positive rate (∼4%) and high true positive rate (>80%) in simulations with large differences in allele frequencies between cases and controls. Application of PopCluster to data from genetic studies of longevity discovers ethnicity-dependent heterogeneity in the association of rs3764814 (USP42) with the phenotype. AVAILABILITY AND IMPLEMENTATION: PopCluster was implemented using the R programming language, PLINK and Eigensoft software, and can be found at the following GitHub repository: https://github.com/gurinovich/PopCluster with instructions on its installation and usage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Methods for inference and comparison of biological networks are emerging as powerful tools for the identification of groups of tightly connected genes whose activity may be altered during disease progression or due to chemical perturbations. Connectivity-based comparisons help identify aggregate changes that would be difficult to detect with differential analysis methods comparing individual genes. METHODS: In this study, we describe a pipeline for network comparison and its application to the analysis of gene expression datasets from chemical perturbation experiments, with the goal of elucidating the modes of actions of the profiled perturbations. We apply our pipeline to the analysis of the DrugMatrix and the TG-GATEs, two of the largest toxicogenomics resources available, containing gene expression measurements for model organisms exposed to hundreds of chemical compounds with varying carcinogenicity and genotoxicity. RESULTS: Starting from chemical-specific transcriptional networks inferred from these data, we show that the proposed comparative analysis of their associated networks identifies groups of chemicals with similar functions and similar carcinogenicity/genotoxicity profiles. We also show that the in-silico annotation by pathway enrichment analysis of the gene modules with a significant gain or loss of connectivity for specific groups of compounds can reveal molecular pathways significantly associated with the chemical perturbations and their likely modes of action. CONCLUSIONS: The proposed pipeline for transcriptional network inference and comparison is highly reproducible and allows grouping chemicals with similar functions and carcinogenicity/genotoxicity profiles. In the context of drug discovery or drug repositioning, the methods presented here could help assign new functions to novel or existing drugs, based on the similarity of their associated network with those built for other known compounds. Additionally, the method has broad applicability beyond the uses here described and could be used as an alternative or as a complement to standard approaches of differential gene expression analysis.

MOTIVATION: Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts-controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics. RESULTS: Here, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens. AVAILABILITY AND IMPLEMENTATION: Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.

The emerging, ubiquitous Internet of Services scenario discloses a radical change in the process of content creation and service consumption. Thus, platforms for service and content provisioning should cope with several issues concerning mobility, context awareness and content adaptation. However, traditional programming paradigms, though proving their effectiveness in mastering a good separation of concerns, fall short when it comes to capture concerns that span and orthogonally crosscut several system components. In service-oriented architecture models that face the extremely dynamical and ever-changing above scenario, it is common and usual an undesirable tangling of functionalities which induces poor flexibility, accuracy and consistency. This paper investigates how an innovative and emerging methodology, Aspect-Oriented Programming, can address these issues in a middleware platform for ubiquitous dynamic context-driven service provisioning and configuration. We also distill some design principles and implementation details of the reengineering activities we accomplished.

The main drivers of today's automotive applications are the reductions of ECU systems cost as well as the development of new innovative functions such as: perceived vehicle performances, active vehicle safety, new emission laws (EU5, EU6) and CO2reduction. On the other hand more and more resources are spent on adapting the Intellectual Properties (IP's) control solutions to different electronic control unit eroding precious time for the development and validation of the project. To match challenging Time to Market constraints (less than 18 months for a new application) versus Automotive functions complexity increasing, without sacrificing system quality and robustness, a new approach appears unavoidable, perhaps a change of paradigm, where relationships and cooperation between Original Equipment Manufacturer (OEMs) and Suppliers as well as Supplier to Supplier interfacing have to evolve into a new model for both development and commercial issues. This emerging scenario has been taken into account by the AUTOSAR (AUTomotive Open System ARchitecture) consortium defining technologies and process in order to permit to share SW IP's over different platforms and applications. This paper describes, using a real example of a mechatronic scalable control IP's, developed with model based approach technique and configurable on different applications using the AUTOSAR technology, the approach followed by Magneti Marelli (MM) to play the new roles of SW IP's supplier and SW IP's integrator.

The adoption of model based design coupled with model compilers capable to generate production quality software code greatly improves the time-to-market and the capability to cope with the complexity of modern embedded controllers for automotive. As side-effect control and software engineers are in charge to deliver, maintain, and trace an increased amount of electronic outcomes such as executable models of controllers and systems, simulation test patterns, and acquisition data streams. Ensuring consistency of wide work-product collections, traceability towards stakeholder requirements, and compliancy with process assessment models requires the adoption of a development process in which model based development tool-chain deeply integrates with configuration and change management tools. This paper describes the approach and the actions that lead to the extension of a well established development process of hand-coded software to the new roles and skills introduced by model based design and the impact on the overall configuration and change management strategy.

In the modern Internet of services scenarios, users need to create, share and access contents and services in extremely personalized ways and by means of heterogeneous devices and interaction channels. The most promising architectural solutions to cope with such dynamic and ever-growing scenario adopt a service oriented approach and leverage service composition platforms to flexibly arrange new value-added services made up of basic off-the-shelf components. Though, current solutions for service composition target rather static scenarios and scarcely support today's dynamic Web, where new services and contents continually become available to satisfy novel user needs and service compositions may become obsolete and need reconfiguration. We propose a novel composition model that aims at being extremely flexible and extensible, yet remaining easily usable by hiding complexity to users.

In the emerging ubiquitous Internet scenario users require to access services and contents from anywhere, at anytime and with any device. Due to this requirement, platforms for service and content provisioning have to address several problems related to the new issues of mobility, multimodality, context awareness and content adaptation. However, current ubiquitous service provisioning platforms still suffer a main drawback: they often underestimate platform logic complexity when multiple ubiquity features should be provided. As a result, the proposed solutions lack a unified approach to the ubiquity issues and provide only limited sets of features. We claim that an integrated and comprehensive solution can stem from a simplicity principle: our approach pushes ubiquity features outside the core middleware layer, by keeping only management and coordination responsibilities. That succeeds in making the middleware design clearer and neater. In this paper we present the key architectural aspects of our middleware platform for ubiquitous dynamic context-driven service provisioning and reconfiguration. We also provide implementation details and description of typical use cases our platform successfully realizes.

MOTIVATION: An important step in analyzing expression profiles from microarray data is to identify genes that can discriminate between distinct classes of samples. Many statistical approaches for assigning significance values to genes have been developed. The Comparative Marker Selection suite consists of three modules that allow users to apply and compare different methods of computing significance for each marker gene, a viewer to assess the results, and a tool to create derivative datasets and marker lists based on user-defined significance criteria. AVAILABILITY: The Comparative Marker Selection application suite is freely available as a GenePattern module. The GenePattern analysis environment is freely available at http://www.broad.mit.edu/genepattern.

In this paper, we present and discuss our experience in the task of probability elicitation from experts for the purpose of belief network construction. In our study, we applied four techniques. Three of these techniques are available from the literature, whereas the fourth one is a technique that we developed by adapting a method for the assessment of preferences to the task of probability elicitation. The new technique is based on the analytic hierarchy process (AHP) proposed by Saaty (1980, 1994), and it allows for the quantitative assessment of the expert inconsistency. The method is, in our opinion, very promising and lends itself to be applied more extensively to the task of probability elicitation.