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Welcome to Ignet!

In 1965 Wheelock et al. first reported Interferon-gamma (IFN-γ)-like virus inhibitor induced in supernatant fluid of cultures of fresh human leukocytes following incubation with phytohemagglutinin. In early 1970s, IFN-γ was further studied, and its name was eventually designated. IFN-γ is the only type II IFN family member. It is secreted by activated immune cells - primarily T and NK cells, but also B-cells, NKT cells and professional antigen presenting cells. IFN-γ is vital in immune defense against bacterial and viral infections and tumor. IFN-γ has been widely studied and found critical in anti-infectious host defense, inflammatory conditions, cancer, and auto-immune diseases. The most striking phenotype from mice lacking either IFN-γ or its receptor is increased susceptibility to bacterial and viral pathogens. IFN-γ is also critical for tumor immuno-surveillance as assessed using spontaneous, transplantable and chemical carcinogen-induced experimental tumors. Additionally, IFN-γ is found important in leukocyte homing, cellular adhesion, immunoglobulin class switching, T helper cell polarity, antigen presentation, cell cycle arrest and apoptosis, neutrophil trafficking and NK cell activation. It also regulates various immune responses that are often critical for induction of protective immunity generated by vaccines.

Recently we have proposed a  literature-based discovery (LBD) approach based on integrating text mining with network centrality analysis to study IFN-γ and vaccine-mediated gene interaction networks. Our approach uses a natural language processing and machine learning based method to automatically extract gene interaction networks from the biomedical literature. To rank the genes in the literature-mined networks and to identify the most important ones we analyze the networks from centrality perspective. We calculate four different types of centralities:

  • degree centrality (the number of neighbors of a node),
  • eigenvector centrality (function of the centralities of its neighbors),
  • closeness centrality (inverse sum of the distances from the node to the other nodes in the network), and
  • betweenness centrality (the proportion of the shortest paths between all the pairs of nodes that pass through the node in interest). Different centralities measure different levels of importance. For example, in betweenness centrality a node is considered important if it occurs on many shortest paths between other nodes, whereas in degree centrality a node is considered important if it is connected to many other nodes.

It has been requested by the community for us to develop a web server to store the analyzed data and provide a user-friendly web interface to query and visualize the analyzed data. To address this request, we have developed Ignet, a user-friendly web interface for the analyses of IFNG gene networks.

One novel feature in Ignet is that its development is accompanied with our development of the Interaction Network Ontology (INO). INO contains more than 800 interaction keywords organized in a hierarchical structure. These terms are organized in INO using a hierarchical structure and aligned with the Basic Formal ontology (BFO; http://www.ifomis.org/bfo). An example of INO term is “increase”, whose parent term in INO is “positive regulation”, which is a child term of “regulation” and “interaction”. In INO, 21 words were listed as “synonym” for the term “increase”, for example, increased, increasing, elevated, and enhanced. These terms were all used for literature retrieval.

The Ignet results are updated monthly. In each dynamic update, Ignet will get new data from the NCIBI BioNLP database, extract the gene-gene relationships, execute the centrality analysis, and store the results in Ignet database for users’ query.

The novelty of Ignet is two-fold. First, Ignet is the first web-based literature mining system that is based on network centrality analysis. Second, Ignet uses the newly developed INO to explore the interaction types found in the extracted gene-gene interaction sentences. The ontology-based approach makes it more powerful to recognize and cluster various interaction types.