PMC:1852316 / 3606-5851 JSONTXT

{"project":"2_test","denotations":[{"id":"17397539-11172013-1689477","span":{"begin":198,"end":199},"obj":"11172013"},{"id":"17397539-11836211-1689478","span":{"begin":237,"end":238},"obj":"11836211"},{"id":"17397539-14673099-1689479","span":{"begin":413,"end":414},"obj":"14673099"},{"id":"17397539-12843395-1689480","span":{"begin":850,"end":851},"obj":"12843395"},{"id":"17397539-11700600-1689481","span":{"begin":1178,"end":1179},"obj":"11700600"},{"id":"17397539-9697168-1689482","span":{"begin":1400,"end":1401},"obj":"9697168"},{"id":"17397539-10475062-1689483","span":{"begin":1520,"end":1522},"obj":"10475062"},{"id":"17397539-11847080-1689484","span":{"begin":1641,"end":1643},"obj":"11847080"},{"id":"17397539-14668243-1689485","span":{"begin":1644,"end":1646},"obj":"14668243"},{"id":"17397539-11099257-1689486","span":{"begin":2241,"end":2243},"obj":"11099257"}],"text":"There have been numerous approaches to TRN inference from gene expression data. Most studies considered gene-gene networks rather than gene-TF networks. Among them are principal component analysis [4] and independent component analysis [5]. Network component analysis (NCA) is a TF-based methodology which differs from other techniques in that the structure of the gene regulatory network is assumed to be known [6]. Therefore, NCA's use is limited to cases in which the network is fairly well known and has strong structural limitations. In reality, only an incomplete and possibly biased TRN is available due to the limited spectrum of experimental conditions imposed. Gardner et al. proposed a methodology to construct the gene-gene control network structure for small networks using microarray data, limiting the number of interactions per gene [7]. We tested a similar approach for large networks and showed that even when there are just a few interactions per gene, there can be thousands of networks that can explain the same microarray data with essentially the same accuracy. Kyoda et al. developed a methodology that employs mutation experiments to arrive at the TRN [8]. However, it is questionable whether their approach can be applied to large TRNs. Liang et al. presented a methodology for Boolean networks and applied it to a small 50 gene system with at most 3 interactions per gene [9]. Boolean networks are an oversimplification of gene expression as they use a binary approximation (fully on or off) [10]. Cluster analysis is based on statistical techniques wherein correlations are sought between the responses of genes [11,12]. However the coordination can be extremely complex and circuitous, i.e. genes may be involved in a multi-branch feedback loop with several TFs made or activated/deactivated by the proteins they encode. These time-delayed, complex relationships are revealed by our methodology as it discovers and quantifies many of these feedback relationships. Although cluster analysis might suggest groups of genes that may be involved in related pathways, it is not an accurate methodology to suggest gene/TF interactions. D'haeseleer et al. applied clustering based on the correlation of microarray data [13]."}