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您的检索式：作者名="Stefano Perna"
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| 1 | The role of transthoracic echocardiography in the diagnosis and management of acute type A aortic syndrome显示文摘 | Moreno Cecconi Fabio Chirillo Carlo Costantini Gianfranco Iacobone Ercole Lopez Raffaele Zanoli Alberto Gili Stefano Moretti Marcello Manfrin Christopher Münch Lucia Torracca Gian Piero Perna | 2012 | American Heart Journal2012，，1： | 2 |
| 2 | Synthesis of Isophoric Sparse Array Allowing Zoomable Beams and Arbitrary Coverage in Satellite Communications 显示文摘 | Ovidio Mario Bucci Stefano Perna Daniele Pinchera | 2015 | IEEE Transactions on Antennas and Propagation2015，63，4： | 1 |
| 3 | A Closer Look at Incidental Findings on Cardiac Computed Tomography显示文摘 | Stefano Bartoletti Francesco Perna Pasquale Santangeli Michela Casella | 2010 | Journal of the American College of Cardiology2010，，： | 1 |
| 4 | TICA: Transcriptional Interaction and Coregulation Analyzer显示文摘Transcriptional regulation is critical to cellular processes of all organisms. Regulatory mechanisms often involve more than one transcription factor(TF) from different families, binding together and attaching to the DNA as a single complex. However, only a fraction of the regulatory partners of each TF is currently known. In this paper, we present the Transcriptional Interaction and Coregulation Analyzer(TICA), a novel methodology for predicting heterotypic physical interaction of TFs. TICA employs a data-driven approach to infer interaction phenomena from chromatin immunoprecipitation and sequencing(ChIP-seq) data. Its prediction rules are based on the distribution of minimal distance couples of paired binding sites belonging to different TFs which are located closest to each other in promoter regions. Notably, TICA uses only binding site information from input ChIP-seq experiments, bypassing the need to do motif calling on sequencing data. We present our method and test it on ENCODE ChIP-seq datasets, using three cell lines as reference including HepG2, GM12878, and K562. TICA positive predictions on ENCODE ChIP-seq data are strongly enriched when compared to protein complex(CORUM) and functional interaction(BioGRID) databases. We also compare TICA against both motif/ChIP-seq based methods for physical TF–TF interaction prediction and published literature. Based on our results, TICA offers significant specificity(average 0.902) while maintaining a good recall(average 0.284) with respect to CORUM, providing a novel technique for fast analysis of regulatory effect in cell lines. Furthermore, predictions by TICA are complementary to other methods for TF–TF interaction prediction(in particular, TACO and CENTDIST). Thus, combined application of these prediction tools results in much improved sensitivity in detecting TF–TF interactions compared to TICA alone(sensitivity of 0.526 when combining TICA with TACO and 0.585 when combining with CENTDIST)with little compromise in specificity(specificity 0.760 when combining with TACO and 0.643 with CENTDIST). TICA is publicly available at http://gffzz2c35c4d9332940achxfo6kbwkw56v6vuw.ffgz.tsg.suse.edu.cn/tica/. | Stefano Perna Pietro Pinoli Stefano Ceri Limsoon Wong | 2018 | Genomics, Proteomics & Bioinformatics2018，16，5： | 0 |
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