PMC:1852316 / 10190-11729 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1852316","sourcedb":"PMC","sourceid":"1852316","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1852316","text":"Network definition\nThe TRN we seek to discover is a list of genes for each of which a set of TFs with up/down regulation is provided (bin = +1/-1 for gene i up/down regulated by TF n). The gene-gene regulation network often considered is implied as the components of each TF and the genes that encode them are also included in our TRNs. This TRN definition provides a unifying framework for all the individual TRN discovery methods we developed, as well as a methodology for the integration of multiple methods. We use multiple methodologies to suggest enhanced TRNs based on three hypotheses and a training set TRN to test them. The result of each methodology is weighed proportional to its success rate using the training set. This approach goes beyond studies that focus on gene-gene networks as it provides more detailed information (such as gene A is up regulated by TF B) that can be tested experimentally and used in medical and biotechnical applications. We demonstrate that methodologies such as gene ontology and phylogenic similarity provide better results when a preliminary set of gene/TF interactions is used instead of a training set of gene-gene data. A simple algorithm, described below, is used to calculate gene-TF scores from gene-gene similarity scores and a preliminary TRN. In addition, we use a novel approach to first approximate TF activity profiles using the preliminary TRN and gene expression data, and then using these TF activities to suggest additional gene/TF interactions via a gene-TF correlation scheme.","divisions":[{"label":"title","span":{"begin":0,"end":18}}],"tracks":[]}