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Welcome to Ignet! The Ignet (Integrated gene network) project is a centrality- and ontology-based liteature discovery system for analyzing and visualizing biological gene interaction networks using all PubMed literature papers. Currently Ignet focuses on the literature mining of human gene interaction networks. The Ignet program is generated based on a literature mining strategy named CONDL, which represents the Centrality and Ontology-based Network Discovery using Literature data. The details about CONDL and its case study application have been described in the papers (Ozgur et al., 2011; Hur et al, 2012).

The CONDL strategy was initially applied to the literature mining of the Interferon-gamma (IFN-γ; Gene symbol: IFNG) and vaccine-mediated gene interaction networks. IFNG is vital in immune defense against bacterial and viral infections and tumor. It also regulates various immune responses that are often critical for induction of protective immunity generated by vaccines. Initially we used a centrality-based literature discovery approach to study IFN-γ and vaccine-mediated gene interaction network. Our study identified indicated a generic IFNG network that contains 1,060 genes and 26,313 interactions among these genes (Reference: Ozgur et al, 2010). As a subset of this generic IFN-γ network, the vaccine-specific subnetwork contains 102 genes and 154 interactions. However, this literature mining strategy misses the identification of those sentences that include specifci vaccine names (e.g., BCG) without mentioning the words "vaccine", "vaccination", or their derivatives. Therefore, we used the VO hierarchy definitions to get more specific vaccine names and their relations, and used them for further literature mining. Our study found that more results were identified (Reference: Ozgur et al., 2011). Then such a CONDL strategy was proposed. Later we used the same CONDL strategy to study the fever and vaccine specific human gene interaction networks (Reference: Hur et al, 2012).

In addition, we also developed an Interaction Network Ontology (INO; Reference: Ozgur et al, 2016). Such an ontology classifies different types of interactions in a hierarchical matter. By using INO, we expect to not only identify different gene-gene interactions, but also identify different interaction types. Ignet constructs gene interaction networks and reports any INO terms as well as VO terms identified from the same documents, which makes the current Ignet particularly useful in vaccine-related gene interaction network research. However, it should noted that the use of Ignet is not limited to such vaccine research and supports for other biomedical ontologies are current in process.

It is noted that the Ignet database contains gene interactions identified with PubMed papers published unitl the end of 2011. We are still processing the literature papers published since 2012.

Ignet provides three programs to support the network analysis: Gene, GenePair, and Dignet. Gene and GenePair allows users to construct and explore the gene interaction networks centered on individual gene or a gene pair, respectively. Dignet (dynamic Ignet) allows construction of gene interaction networks for a specific domain, defined by users' PubMed query.

Your feedback is more than welcome and appreciated!

 

Cite Ignet:

Ozgur A, Hur J, Xiang Z, Ong E, Radev D, and He Y. Ignet: A centrality and INO-based web system for analyzing and visualizing literature-mined networks. August 1-2, 2016, joint session ICBO+BioCreative, at the International Conference on Biomedical Ontologies (ICBO-2016) and BioCreative 2016, August 1-4, 2016, Oregon State University, Corvallis, OR, USA. 2-page proceeding paper (http://ceur-ws.org/Vol-1747/BP01_ICBO2016.pdf).

References:

Ozgur A, Xiang Z, Radev D, and He Y. Mining of vaccine-associated IFN-γ gene interaction networks using the Vaccine Ontology. Journal of Biomedical Semantics. 2011, 2(Suppl 2):S8. PMID: 21624163.

Hur J, Özgür A, Xiang Z, He Y. Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining. Journal of Biomedical Semantics. 2012, 3:18. PMID: 23256563.

Hur J, Özgür A, Xiang Z, and He Y. Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions. Journal of Biomedical Semantics. 2015, 6:2. PMID: 25785184.

Özgür A, Hur J, and He Y. The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature. BioData Mining. 2016, 9:41. PMID: 28031747.


© 2008-2019 University of Michigan. Ignet data and tools are freely available for public use.