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Database containing information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The available information include side effect frequency, drug and side effect classifications as well as links to further information, for example drug-target relations. The SIDER Side Effect Resource represents an effort to aggregate dispersed public information on side effects. To our knowledge, no such resource exist in machine-readable form despite the importance of research on drugs and their effects. The creation of this resource was motivated by the many requests for data that we received related to our paper (Campillos, Kuhn et al., Science, 2008, 321(5886):263-6.) on the utilization of side effects for drug target prediction. Inclusion of side effects as readouts for drug treatment should have many applications and we hope to be able to enhance the respective research with this resource. You may browse the drugs by name, browse the side effects by name, download the current version of SIDER, or use the search interface.
Proper citation: SIDER (RRID:SCR_004321) Copy
http://bioinformatics.biol.uoa.gr/human_gpdb/
A publicly accessible, relational database of human G-Proteins and their interactions with human GPCRs and Effectors. Advanced data integration techniques make Human-gpDB very rich in context since all of the bioentities are linked to a rich variety of external data sources. High quality visualization methods make the networks more informative and the extraction of information easier. Human-gpDB is currently a very useful tool for drug targeting investigation. The sequences of G-Proteins and GPCRs are classified according to a hierarchy of different classes, families and sub-families, whereas the Effectors sequences are classified in families, subfamilies and types, based on extensive literature search. The classification of GPCRs follows the IUPHAR classification, while the Effectors classification is a unique feature and is based on their function. The database currently holds information about 713 human GPCRs, 36 human G-Proteins and 99 human Effectors. The collection of the information about the interactions between these molecules was done manually and the current status of Human-gpDB reveals information about 1663 connections between GPCRs and G-Proteins and 1618 connections between G-Proteins and Effectors.
Proper citation: Human-gpDB (RRID:SCR_006223) Copy
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.
Proper citation: Candidate Genes to Inherited Diseases (RRID:SCR_008190) 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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