ABSTRACT
Latent community discovery that combines links and contents of a text-associated network has drawn more attention with the advance of social media. Most of the previous studies aim at detecting densely connected communities and are not able to identify general structures, e.g., bipartite structure. Several variants based on the stochastic block model are more flexible for exploring general structures by introducing link probabilities between communities. However, these variants cannot identify the degree distributions of real networks due to a lack of modeling of the differences among nodes, and they are not suitable for discovering communities in text-associated networks because they ignore the contents of nodes. In this paper, we propose a popularity-productivity stochastic block (PPSB) model by introducing two random variables, popularity and productivity, to model the differences among nodes in receiving links and producing links, respectively. This model has the flexibility of existing stochastic block models in discovering general community structures and inherits the richness of previous models that also exploit popularity and productivity in modeling the real scale-free networks with power law degree distributions. To incorporate the contents in text-associated networks, we propose a combined model which combines the PPSB model with a discriminative model that models the community memberships of nodes by their contents. We then develop expectation-maximization (EM) algorithms to infer the parameters in the two models. Experiments on synthetic and real networks have demonstrated that the proposed models can yield better performances than previous models, especially on networks with general structures.
ABSTRACT
AIM: ΦC31 integrase mediates site-specific recombination between two short sequences, attP and attB, in phage and bacterial genomes, which is a promising tool in gene regulation-based therapy since the zinc finger structure is probably the DNA recognizing domain that can further be engineered. The aim of this study was to screen potential pseudo att sites of ΦC31 integrase in the human genome, and evaluate the risks of its application in human gene therapy. METHODS: TFBS (transcription factor binding sites) were found on the basis of reported pseudo att sites using multiple motif-finding tools, including AlignACE, BioProspector, Consensus, MEME, and Weeder. The human genome with the proposed motif was scanned to find the potential pseudo att sites of ΦC31 integrase. RESULTS: The possible recognition motif of ΦC31 integrase was identified, which was composed of two co-occurrence conserved elements that were reverse complement to each other flanking the core sequence TTG. In the human genome, a total of 27924 potential pseudo att sites of ΦC31 integrase were found, which were distributed in each human chromosome with high-risk specificity values in the chromosomes 16, 17, and 19. When the risks of the sites were evaluate more rigorously, 53 hits were discovered, and some of them were just the vital functional genes or regulatory regions, such as ACYP2, AKR1B1, DUSP4, etc. CONCLUSION: The results provide clues for more comprehensive evaluation of the risks of using ΦC31 integrase in human gene therapy and for drug discovery.
