Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

Linked open drug data for pharmaceutical research and development.

There is an abundance of information about drugs available on the Web. Data sources range from medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in clinical trials. These data are typically not connected together, which reduces the ease with which insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web Consortium's (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly available data about drugs, created Linked Data representations of the data sets, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task force provides recommendations for the best practices of exposing data in a Linked Data representation. In this paper, we present past and ongoing work of LODD and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing.

Beyond data integration.

Pharmaceutical R&D organizations have no shortage of experimental data or annotation information. However, the sheer volume and complexity of this information results in a paralyzing inability to make effective use of it for predicting drug efficacy and safety. Data integration efforts are legion, but even in the rare instances where they succeed, they are found to be insufficient to advance programs because interpretation of query results becomes a research project in itself. In this review, we propose a coherent, interoperable platform comprising knowledge engineering and hypothesis generation components for rapidly making determinations of confidence in mechanism and safety (among other goals) using experimental data and expert knowledge.

DRAGON and DRAGON view: information annotation and visualization tools for large-scale expression data.

The Database Referencing of Array Genes ONline (DRAGON) database system consists of information derived from publicly available databases including UniGene, SWISS-Prot, Pfam, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Through a Web-accessible interface, the DRAGON Annotate tool rapidly supplies information pertaining to a range of biological characteristics of all the genes in any large-scale gene expression data set. The subsequent inclusion of this information during data analysis and visualization allows for deeper insight into gene expression patterns. The set of DRAGON View tools provides methods for the analysis and visualization of expression patterns in relation to annotated information. Instead of incorporating the standard set of clustering and graphing tools available in many large-scale expression data analysis software packages, DRAGON View has been specifically designed to allow for the analysis of expression data in relation to the biological characteristics of gene sets.

DRAGON View: information visualization for annotated microarray data.

The DRAGON View information visualization tools aid in the comprehensive analysis of large-scale gene expression data that has been annotated with biologically relevant information through the generation of three types of complementary graphical outputs.