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TICA: Transcriptional Interaction and Coregulation Analyzer

Genomics, Proteomics & Bioinformatics 2018年第5期16卷 342-353页

作者： Stefano Perna Pietro Pinoli Stefano Ceri Limsoon Wong Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano School of Computing National University of Singapore

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

关键词： Transcription factors Coregulation Protein–protein interactions Machine learning data-driven analysis

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data-driven Probabilistic Static Security Assessment for Power System Operation Using High-order Moments

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Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1233-1236页

作者： Guanzhong Wang Zhiyi Li Feng Zhang Ping Ju Hao Wu Changsen Feng Department of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Grid Zhejiang Electric Power Research Institute Hangzhou 310014China College of Information Engineering Zhejiang University of TechnologyHangzhou 310008China

In this letter,a new formulation of Lebesgue integration is used to evaluate the probabilistic static security of power system operation with uncertain renewable energy generation.The risk of power flow solutions violating any pre-defined operation security limits is obtained by integrating a semialgebraic set composed of polynomials.With the high-order moments of historical data of renewable energy generation,the integration is reformulated as a generalized moment problem which is then relaxed to a semi-definite program(SDP).Finally,the effectiveness of the proposed method is verified by numerical examples.

关键词： data-driven analysis probabilistic static security assessment power system operation distributionally robust approach

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