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iFpClass|NAViGaTOR | I2D | mirDIP | CDIP |NetwoRx | SCRIPDB |GeneCards mirror | BTSVQ



Software analysis and modeling tools

Our focus is on network analysis and modeling, integrated with cancer profiles that will enable us to identify diagnostic and prognostic biomarkers, understand disease initiation and progression, which will lead to improving cancer treatment. Our tools and resources, such as NAViGaTOR, FpClass, I2D, mirDIP, CDIP, and BTSVQ enable users to interpret integrated cancer profiles, and create relevant models dynamically. We also host a GeneCards mirror.

FpClass - Data mining-based prediction of physical protein interactions.

FpClass -- Comprehensive, data mining-based prediction of physical protein interacions

FpClass - is an association mining algorithm that we used and validated for comprehensive, in silico prediction of physical protein interactions. FpClass is a reliable, validated method for data mining-based prediction of physical protein interactions, and provides 250,542 high confidence interactions among 10,529 human proteins, including 1,089 interactome orphans. Extensive computational and biological validation shows FpClass outperforms existing computational methods and most biological assays in sensitivity and specificity. Using three bioassays we tested 233 high and medium confidence predictions, and validated 137 interactions, including seven novel potential partners of the tumor suppressor p53. Importantly, we validated 5 of these p53 interactions with orphans by GST pull-down assay (5 of 6 tested -- validation rate of 83%). Overall, validation rates were 40% (2/5) for co-IP, 47% (14/30) for GST pull-down, and 61% (121/198) for MaMTH (Petschnigg et al., Nat Methods, 2014). The high validation rate for MaMTH suggests that FpClass could help guide high-throughput screening, in a combined computational-experimental approach to interactome mapping. This substantially extends our interactome work, including I2D (Brown, Jurisica, Genome Biol, 2007) and (Brown, Jurisica, Bioiformatics, 2005). NAViGaTOR (Brown et al., Bioinformatics, 2009) was used for network analysis and visualization.`
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NAViGaTOR-Network Analysis, Visualization, & Graphing TORonto

NAViGaTOR-Network Analysis, Visualization, & Graphing TORonto NAViGaTOR is a software package for iscalable, interactive visual data mining - visualization and analysis of large, typed graphs. These networks could be protein-protein interaction networks, microRNA:gene or transcriptional regulatory networks, metabolic networks, or other graphs, such as transportationa networks, communication networks or even solar system. NAViGaTOR can query OPHID / I2D - online databases of interaction data - as well as PSICQUIC, KEGG, Reactome and other data sources, as well as link annotation from Uniprot, GO, Pubmed, and display networks in 2D or 3D. To improve scalability and performance, NAViGaTOR combines Java with OpenGL to provide a 2D/3D visualization system on multiple hardware platforms. NAViGaTOR also provides analytical capabilities and supports standard import and export formats such as GO and the Proteomics Standards Initiative (PSI).

In protein-protein interaction networks, nodes represent proteins, and edges between nodes represent physical interactions between the proteins.These visualizations can enable insights into the proteins that play key roles in diseases such as cancer.
Go to NAViGaTOR home page

I2D-Interologous Interaction Database

I2D-Interologous Interaction Database

I2D is an on-line database of known and predicted mammalian and eukaryotic protein-protein interactions. It has been built by mapping high-throughput (HTP) data between species. Thus, until experimentally verified, these interactions should be considered "predictions". I2D remains one of the most comprehensive sources of known and predicted eukaryotic PPI.
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miRDIP - microRNA:target prediction Data Integration Portal

mirDIP is an on-line database that integrates twelve microRNA prediction datasets from six microRNA prediction databases, allowing users to customize their microRNA target searches. Combining microRNA predictions allows users to obtain more robust target predictions, providing more confidence in the microRNA targets.

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CDIP - Cancer Data Integration Portal

CDIP is an on-line database of significantly deregulated genes in lung, ovarian, prostate and head&neck cancers. Work on pancreas cancer and sarcoma is ongoing.
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NetwoRx - A database for linking drugs to pathways and networks

NetwoRx stores pre-computed drug lists for KEGG pathways, GO categories, YEASTRACT transcription factor targets, and orthologs of human KEGG DISEASE groups. Users can interactively explore or download pathway-drug, pathwaypathway, and drug-drug networks, or submit a new gene set to NetwoRx and retrieve the drugs that target it.
Go to NetwoRx home page





SCRIPDB - A Portal for Easy Access to Syntheses,Chemicals, and Reactions In Patents

SCRIPDB provides the full original patent text, reactions, and relationships described within any individual patent, in addition to the molecular files
common to structural databases. We discuss how such information is valuable in medical text mining, chemical image analysis, reaction extraction,
and in silico pharmaceutical lead optimization. SCRIPDB may be searched by exact chemical structure, substructure, or molecular similarity
and the results may be restricted to patents describing synthetic routes.
Go to SCRIPDB home page





GAP Portal: Integrative approach to predicting gene functional associations using using novel semantic similarity measure


GAP (Gene functional Association Predictor) is an integrative method for predicting and characterizing gene functional associations. It integrates different biological features using a novel taxonomy-based semantic similarity measure in predicting and prioritizing high-quality putative gene associations. The proposed similarity measure increases information gain from the available gene annotations. The annotation information is incorporated from several public pathway databases, Gene Ontology annotations as well as drug and disease associations from the scientific literature.

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GeneCards mirror

GeneCards mirror provides a comprehensive annotation of individual genes.
Go to GeneCards mirror page


BTSVQ-Binary tree structured vector quantization

BTSVQ-Binary tree structured vector quantization

BTSVQ is a computational tool to analyze and visualize microarray gene expression data. This technique merges the results of SOM (genes space), and partitive k-means (specimen space). The algorithm uses vector quantization and self-organizing capabilities of SOMs in finding significant gene centers in gene space (high dimensionality and large number of clusters), and the effectiveness of k-means in experiment space (medium dimensionality and low number of clusters).
Go to BTSVQ home page