M
Michael Q. Zhang
Researcher at Tsinghua University
Publications - 396
Citations - 46412
Michael Q. Zhang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Gene & Chromatin. The author has an hindex of 93, co-authored 378 publications receiving 42008 citations. Previous affiliations of Michael Q. Zhang include Chinese Academy of Sciences & Peking Union Medical College Hospital.
Papers
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Journal ArticleDOI
Identifying cooperativity among transcription factors controlling the cell cycle in yeast.
TL;DR: A novel approach that reveals how multiple TFs cooperate to regulate transcription in the yeast cell cycle by integrating genome-wide gene expression data and chromatin immunoprecipitation data to discover more biologically relevant synergistic interactions between different TFs and their target genes than previous studies.
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Identifying combinatorial regulation of transcription factors and binding motifs
Mamoru Kato,Mamoru Kato,Naoya Hata,Nilanjana Banerjee,Nilanjana Banerjee,Bruce Futcher,Michael Q. Zhang +6 more
TL;DR: A novel method that integrates chromatin immunoprecipitation data with microarray expression data and with combinatorial TF-motif analysis is used, finding that the pairwise combination of a TF for an early cell-cycle phase and aTF for a later phase is often used to control gene expression at intermediate times.
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Large-scale gene expression data analysis: a new challenge to computational biologists.
TL;DR: In this survey, three recent experiments related to transcriptional regulation are reviewed and the great challenge for computational biologists trying to extract functional information from large-scale gene expression data is discussed.
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The Transcription Factor Foxo1 Controls Central-Memory CD8+ T Cell Responses to Infection
Myoungjoo V. Kim,Weiming Ouyang,Will Liao,Will Liao,Michael Q. Zhang,Michael Q. Zhang,Ming O. Li,Ming O. Li +7 more
TL;DR: It is shown that mice lacking the transcription factor Foxo1 in activated CD8+ T cells have defective secondary, but not primary, responses to Listeria monocytogenes infection.
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SCALE method for single-cell ATAC-seq analysis via latent feature extraction.
Lei Xiong,Kui Xu,Kang Tian,Yanqiu Shao,Lei Tang,Ge Gao,Michael Q. Zhang,Tao Jiang,Qiangfeng Cliff Zhang +8 more
TL;DR: SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation, and generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATac-seq experiments.