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QUADrATiC / QUB Accelerated Drug And Transcriptomic Connectivity

A user-friendly tool for the exploration of gene expression connectivity on the subset of the library of integrated cellular signatures data set corresponding to FDA-approved small molecule compounds. QUADrATiC enables the identification of compounds for repurposing therapeutic potentials. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

Cogena / Co-expressed gene-set enrichment analysis

A fully configurable framework for co-expressed gene set enrichment analysis. By combining pathway analysis and drug repositioning analysis, Cogena provides a unique approach to imply the drug mode of action in a disease context, which is important to the translational development of computationally repositioned drugs. Cogena is a powerful tool for co-expressed gene set enrichment analysis, including pathway analysis and drug repositioning.

sscMap / statistically significant connections' map

A Java application designed to undertake connectivity mapping tasks. sscMap is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies.

ProbCMap / Probabilistic connectivity mapping

A probabilistic drug connectivity mapping method that can be used to infer drug similarities based on drug treatment gene expression measurements from multiple cell lines, such as the Connectivity Map data. ProbCMap is superior to alternatives in finding functionally and chemically similar drugs from the Connectivity Map data set. The Group Factor Analysis model is first applied to the expresssion data, separating cell line-specific responses from those that are shared by two or more cell lines. Drug similarities are then computed in the captured model space. In addition to single drug retrieval, also combinatorial retrieval is possible, searching for pairs of drugs that explain the query drug well.


A web-server used to identify Anatomical Therapeutic Chemical (ATC) class. iATC-mISF was developed by incorporating the information of chemical-chemical interaction, the information of the structural similarity, and the information of the fingerprintal similarity. To maximize the convenience for most experimental scientists, a step-by-step guide was provided, by which users can easily get their desired results without needing to go through the complicated mathematical equations.

GSLHC / Gene-Set Local Hierarchical Clustering

A platform which is based on a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. GSLHC can be used for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.