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Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks.

Much can be learned about the progress, fathers and future of a scientific domain from the analysis of a collection of relevant articles and their corresponding authors. Here, we study the highly interdisciplinary domain of Artificial Immune System (AIS) since its birth, a couple of decades ago. We apply Social Network Analysis to the coauthorship network of the most comprehensive publicly accessible AIS bibliography. We automatically extract publication dates and author names from the bibliography and evaluate authors with the highest degree (unique collaborations) and centrality (influence). Our results highlight the relative growth of publication volume and identify significant contributors in the AIS field. Furthermore, our findings are not only encouraging for the AIS community but may be useful for analyses of other scientific communities and leading contributors therein.

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