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Publication and archival of scientific results is still commonly considered the responsability of classical publishing companies. Classical forms of publishing, however, which center around printed narrative articles, no longer seem well-suited in the digital age. In particular, there exist currently no efficient, reliable, and agreed-upon methods for publishing scientific datasets, which have become increasingly important for science. In this article, we propose to design scientific data publishing as a Web-based bottom-up process, without top-down control of central authorities such as publishing companies. Based on a novel combination of existing concepts and technologies, we present a server network to decentrally store and archive data in the form of nanopublications, an RDF-based format to represent scientific data. We show how this approach allows researchers to publish, retrieve, verify, and recombine datasets of nanopublications in a reliable and trustworthy manner, and we argue that this architecture could be used as a low-level data publication layer to serve the Semantic Web in general. Our evaluation of the current network shows that this system is efficient and reliable.

Open PHACTS is a public-private partnership between academia, publishers, small and medium sized enterprises and pharmaceutical companies. The goal of the project is to deliver and sustain an 'open pharmacological space' using and enhancing state-of-the-art semantic web standards and technologies. It is focused on practical and robust applications to solve specific questions in drug discovery research. OPS is intended to facilitate improvements in drug discovery in academia and industry and to support open innovation and in-house non-public drug discovery research. This paper lays out the challenges and how the Open PHACTS project is hoping to address these challenges technically and socially. © 2012 Elsevier Ltd.

Making available and archiving scientific results is for the most part still considered the task of classical publishing companies, despite the fact that classical forms of publishing centered around printed narrative articles no longer seem well-suited in the digital age. In particular, there exist currently no efficient, reliable, and agreed-upon methods for publishing scientific datasets, which have become increasingly important for science. Here we propose to design scientific data publishing as a Web-based bottom-up process, without top-down control of central authorities such as publishing companies. Based on a novel combination of existing concepts and technologies, we present a server network to decentrally store and archive data in the form of nanopublications, an RDF-based format to represent scientific data. We show how this approach allows researchers to publish, retrieve, verify, and recombine datasets of nanopublications in a reliable and trustworthy manner, and we argue that this architecture could be used for the Semantic Web in general. Evaluation of the current small network shows that this system is efficient and reliable., Comment: In Proceedings of the 14th International Semantic Web Conference (ISWC) 2015

Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.

The increasing and unprecedented publication rate in the biomedical field is a major bottleneck for knowledge discovery in the Life Sciences. The manual curation of facts from published scientific papers is slow and inefficient, and therefore new approaches are needed that can enable the automatic, scalable and reliable extraction of assertions. While the publication of scientific assertions and datasets on the SemanticWeb is gaining traction, it also creates new challenges such as the proper representation of provenance and versioning. Here, we address these issues and describe our efforts to represent the DisGeNET database of human gene-disease associations as permanent, immutable, and provenance rich digital objects called nanopublications. Our nanopublications are the first instance of a Linked Data model that ensures stable interlinking of the assertion and its metadata by Trusty URIs. As DisGeNET integrates manually curated as well as text-mined data of different origins, the semantic description of the evidence for each assertion is important to provide trust and allow evidence-based hypothesis generation. Here, we describe our steps to ensure high quality and demonstrate the utility of linking our data to other datasets on the emerging Semantic Web.

The discovery of new medicines requires pharmacologists to interact with a number of information sources ranging from tabular data to scientific papers, and other specialized formats. In this application report, we describe a linked data platform for integrating multiple pharmacology datasets that form the basis for several drug discovery applications. The functionality offered by the platform has been drawn from a collection of prioritised drug discovery business questions created as part of the Open PHACTS project, a collaboration of research institutions and major pharmaceutical companies. We describe the architecture of the platform focusing on seven design decisions that drove its development with the aim of informing others developing similar software in this or other domains. The utility of the platform is demonstrated by the variety of drug discovery applications being built to access the integrated data. An alpha version of the OPS platform is currently available to the Open PHACTS consortium and a first public release will be made in late 2012, see http://www.openphacts.org/ for details. © 2014 - IOS Press and the authors.

Molecular information systems play an important part in modern data-driven drug discovery. They do not only support decision making but also enable new discoveries via association and inference. In this review, we outline the scientific requirements identified by the Innovative Medicines Initiative (IMI) Open PHACTS consortium for the design of an open pharmacological space (OPS) information system. The focus of this work is the integration of compound-target-pathway-disease/phenotype data for public and industrial drug discovery research. Typical scientific competency questions provided by the consortium members will be analyzed based on the underlying data concepts and associations needed to answer the questions. Publicly available data sources used to target these questions as well as the need for and potential of semantic web-based technology will be presented. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. [115191], resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and inkind contribution of EFPIA companies.

The Open PHACTS proiect is a large-scale public-private partnership funded under the European Innovative Medicines Initiative. The aim of the project is to create a stable infrastructure that combines diverse biomedical databases and enables scientists to answer complex questions of relevance to drug discovery and human health. The data integration is achieved through semantic web technologies, with data modeled in RDF and served through a high performance triple store. The main point of interaction with the system is an API, designed to be “developer friendly” by using familiar web technologies such as REST services and JSON. The vision is the Open PHACTS API becomes the foundation of an “application ecosystem”, enabling anyone to create applications targeting different use-cases within this domain. Here, we present two examples of the use of the Open PHACTS API to create mobile scientific applications. The ChemBioNavigator is a tool that allows researchers to analyze and triage sets of bioactive small molecules. Physiochemical properties are used to separate the molecules into different areas of a graph, upon which biological information from the Open PHACTS database is overlaid. This visualization allows users to understand the relationship between these data and select the molecules with the best overall combinations of these properties. The second example is the iPharm application which is designed to address a common situation where a researcher needs a high-level overview on a drug or target. The user simply opens up the application, enters a drug name and is presented with key clinical and molecular data summarizing the action of that drug. An important aspect of both applications is that while they are based on semantic technologies, the user interfaces are specifically designed to look like “normal” applications. We demonstrate the combination of semantic data with natural user interfaces provides a powerful mechanism to address scientific data navigation.