It demonstrates a top down integrative approach to modeling hormone protein interaction network. Literature mining Our retrieval system was composed of two software com ponents, the dictionary based text mining tool, ProMiner, selleck catalog and the semantic search engine, SCAIView. The ProMiner software uses dictionaries for the recognition of a variety of different terminologies including gene, protein, disease, SNP, drug, etc. The SCAIView software deploys the results from ProMiner annotations, ranks the extracted genes Inhibitors,Modulators,Libraries proteins based on relative entropy scoring function, and displays the named entities by tagging them through the text. SCAIView aca demic version can be freely accessed at bishop. scai. fraunhofer. de scaiview. PubMed abstracts were searched for all instances of genes and proteins, which are men tioned in the context of dementia as keyword.

The retrieved entities were manually checked for their true relevance to both hormones and dementia in the context of their abstracts. Network reconstruction and annotation The results from text mining were cross checked with the contents of the EndoNet database and the con firmed entities were used as seed Inhibitors,Modulators,Libraries proteins in the BIANA tool for reconstruction Inhibitors,Modulators,Libraries of the dementia related hormonal network at level 2. The initial protein protein interaction network was constructed around the seed set. In order to reduce the dimensionality and increase the confidence, interactions that are only supported by the yeast two hybrid method were removed and those interac tions that are independently confirmed by two or more ex perimental methods were maintained.

The network was visualized and statistically analyzed in the Cytoscape and Gephi environments. G lay clustering algorithm was used for modularity analysis. Pathways used for the recovery test For the pathway recovery Inhibitors,Modulators,Libraries test, we obtained the following expert curated hormonal pathways, used them as gold standard, and mapped them onto the network, growth hormone pathway, insulin signaling pathway, leptin signaling pathway, thyroid hormone signaling, regulation of the estrogen Inhibitors,Modulators,Libraries receptor pathway, corticotropin releasing hormone pathway, and Mela tonin signaling pathway. Statistical analysis Gene set enrichment analysis was performed using the Molecular Signature Database. DAVID functional annotation tool was used for annotation of differentially expressed genes in the network.

Translational validation For establishing the clinical relevance of the core DHN model, knockout mouse phenotypes were retrieved from MGI database. For retrieval and extraction of puta tive biomarker information selleckbio from the literature, bio marker terminology was used. Pathway membership for each target was obtained from KEGG database and their association to disease was determined using genetic association database.