Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data

Chen, Bin; Dong, Xiao; Jiao, Dazhi; Wang, Huijun; Zhu, Qian; Ding, Ying; Wild, David

doi:10.1186/1471-2105-11-255

Cited by 196 publications

(159 citation statements)

References 37 publications

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“…This semantic approach to data integration has been pioneered by other research efforts in the biomedical domain (Belleau et al, 2008;Chen et al, 2010;Hardy et al, 2010;Hassanzadeh et al, 2009) . The platform provides a semantically integrated view of the data by exploiting an identity mapping service (IMS) to construct appropriate responses based on the contextual aspects of each query.…”

Section: Methodsmentioning

confidence: 99%

EMBnet.journal 19 Suppl. B

journal¹

2013

EMBnet j.

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Section: Methodsmentioning

confidence: 99%

EMBnet.journal 19 Suppl. B

journal¹

2013

EMBnet j.

View full text Add to dashboard Cite

“…This semantic approach to data integration has been pioneered by other research efforts in the biomedical domain (Belleau et al, 2008;Chen et al, 2010;Hardy et al, 2010;Hassanzadeh et al, 2009) The platform provides a semantically integrated view of the data by exploiting an identity mapping service (IMS) to construct appropriate responses based on the contextual aspects of each query. For instance, will users differentiate to the same level of granularity?…”

Section: Methodsmentioning

confidence: 99%

Mobile applications driven by Open PHACTS semantic web technology

Chichester

Harald²,

Harder

2013

EMBnet j.

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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.

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“…The graph itself is laid out using GraphViz. 8 Stronger connections are denoted with darker lines. Thicker borders around bundles denote those with more assays, and each bundle's width is proportional to the logarithm of the number of molecules tested.…”

Section: Project-oriented View Of Pubchemmentioning

confidence: 99%

Automatically Detecting Workflows in PubChem

et al. 2012

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Public databases that store the data from small-molecule screens are a rich and untapped resource of chemical and biological information. However, screening databases are unorganized, which makes interpreting their data difficult. We propose a method of inferring workflow graphs-which encode the relationships between assays in screening projects-directly from screening data and using these workflows to organize each project's data. On the basis of four heuristics regarding the organization of screening projects, we designed an algorithm that extracts a project's workflow graph from screening data. Where possible, the algorithm is evaluated by comparing each project's inferred workflow to its documentation. In the majority of cases, there are no discrepancies between the two. Most errors can be traced to points in the project where screeners chose additional molecules to test based on structural similarity to promising molecules, a case our algorithm is not yet capable of handling. Nonetheless, these workflows accurately organize most of the data and also provide a method of visualizing a screening project. This method is robust enough to build a workfloworiented front-end to PubChem and is currently being used regularly by both our lab and our collaborators. A Python implementation of the algorithm is available online, and a searchable database of all PubChem workflows is available at

show abstract

Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data

Cited by 196 publications

References 37 publications

EMBnet.journal 19 Suppl. B

EMBnet.journal 19 Suppl. B

Mobile applications driven by Open PHACTS semantic web technology

Automatically Detecting Workflows in PubChem

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