![]() ![]() ) to access the databases storing FIs and cancer gene index data (see below). The middle server-side application uses hibernate ( The back-end contains several databases hosted in the open-source MySQL database engine ( We used conventional three-tier software architecture to implement ReactomeFIViz (įigure 1). This tool can also be used to perform pathway-based data analysis by using high quality human-curated pathways in the Reactome databaseĤ, the most comprehensive open source pathway database. The FI network was constructed by merging interactions extracted from human curated pathways with interactions predicted using a machine learning approach. This tool uses the highly reliable Reactome functional interaction (FI) networkģ for doing network-based data analysis. In this paper, we describe a software tool called ReactomeFIViz (also called the Reactome FI Cytoscape app or ReactomeFIPlugIn), which can be used to perform pathway- and network-based data analysis for data generated from high-throughput experiments. Pathway- and network-based data analysis approaches project information about seemingly unrelated genes and proteins onto pathway and network contexts, and create an integrated view for researchers to understand mechanisms related to phenotypes of interest. Many studies have shown that alterations in pathways or networks are better correlated with complex disease phenotypes than any particular gene or gene productĢ. A user-friendly software tool is extremely important for both bench and computational biologists to perform high-throughput data analysis related to cancer and other complex diseases. However, extracting reliable and meaningful results from these experiments is usually difficult and requires sophisticated computational tools and algorithms, which are challenging for experimental biologists to comprehend. High-throughput experiments, which generate large and complex data sets, are routinely performed in modern biological and clinical studies to unravel mechanisms underlying complex diseases, such as cancer. ![]() We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. ![]() This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. High-throughput experiments are routinely performed in modern biological studies. ![]()
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