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OrChem is an extension for the Oracle 11G database that adds registration and indexing of chemical structures to support fast substructure and similarity searching. The cheminformatics functionality is provided by the Chemistry Development Kit. OrChem provides similarity searching with response times in the order of seconds for databases with millions of compounds, depending on a given similarity cut-off. For substructure searching, it can make use of multiple processor cores on today''s powerful database servers to provide fast response times in equally large data sets. OrChem is an Oracle chemistry plug-in using the Chemistry Development Kit (CDK). The CDK is an open source Java library for Chemoinformatics and Bioinformatics. OrChem is maintained by the chemoinformatics and metabolism team of the European Bioinformatics Institute. Oracle Data cartridges extend the capabilities of the Oracle server. For chemistry various commercial cartridges exist that facilitate searching and analyzing chemical data. OrChem also provides functionality like this, but is not a cartridge. It doesn''t need Oracle''s extensibility architecture because its Java components run as Java stored procedures inside the Oracle standard JVM (Aurora). OrChem is suitable for Oracle 11G and onwards. Starting with Oracle 11g release 1 (11.1) there is a just-in-time(JIT) compiler for Oracle JVM environment. A JIT compiler for Oracle JVM enables much faster execution because it manages the invalidation, recompilation, and storage of code without an external mechanism. This new Oracle feature makes Java classes perform better than before.
Proper citation: OrChem (RRID:SCR_008865) Copy
https://compbio.dfci.harvard.edu/predictivenetworks//
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 regression-based network inference algorithm creates a graph of gene interactions in which cycles may be present (but no self-loops). Based on information-theoretic techniques, a causal gene interaction network is inferred from both prior knowledge (interactions extracted from biomedical literature and structured biological databases) and gene expression data. A prediction model is fitted for each gene, given its parents, enabling assessment of the predictive ability of the network model.
Proper citation: Predictive Networks (RRID:SCR_006110) Copy
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The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
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