ゲノム情報科学研究教育機構  アブストラクト
Date February 6, 2008
Speaker Dr. Ruisheng Wang, Chinese Academy of Science, Beijing, China
Title Inferring transcriptional interactions and regulator activities from high-throughput data
Abstract    Identifying the relationships between transcription factors (TFs) and their targets from gene expression data is of utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various posttranslational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, which means TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes. In this talk, I describe a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and multiple data sources. In addition, I will also talk about a new technique to combine multiple time-course microarray datasets from different conditions for inferring the TRN in a more accurate manner.
  The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability.

References;
1. Y.Wang, T.Joshi, D.Xu, X-S.Zhang, L.Chen, Inferring Gene Regulatory Networks from Multiple Microarray Datasets, Bioinformatics, 22, 2413 - 2420, 2006.
2. R. Wang, Y.Wang, X-S. Zhang, L.Chen. Inferring Transcriptional Regulatory Networks from High-throughput Data. Bioinformatics, doi:10.1093/bioinformatics/btm465, 2007.
3. R.Wang, Y.Wang, L-Y.Wu, X-S.Zhang, L.Chen. Analysis on Multi-domain Cooperation for Predicting Protein-Protein Interactions. BMC Bioinformatics, 8:391, doi:10.1186/1471-2105-8-391, 2007.
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