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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

The fast rate limit of driven diffusive systems

TL;DR: In this paper, the stationary nonequilibrium states of the van Beijeren/Schulman model of a driven lattice gas in two dimensions were studied, where jumps are much faster in the direction of the driving force than orthogonal to it.
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Correlated evolution of transcription factors and their binding sites

TL;DR: It is shown that the evolutions of the TFs and their TFBSs are significantly correlated across eukaryotes and a mutual information-based method to identify co-evolved protein residues and DNA bases is developed.
Journal ArticleDOI

OSCAR: one-class SVM for accurate recognition of cis-elements

TL;DR: This article proposes a novel approach, OSCAR, which utilizes one-class SVM algorithms, and incorporates multiple factors to aid the recognition of transcription factor binding sites, and finds that the method outperforms existing algorithms, especially in the high sensitivity region.
Journal ArticleDOI

The loss of heterochromatin is associated with multiscale three-dimensional genome reorganization and aberrant transcription during cellular senescence.

TL;DR: In this paper, the authors provided an epigenomic map of heterochromatin reorganization during senescence, showing that both facultative and constitutive heterochematin showed similar and distinct multiscale alterations in the 3D map, chromatin accessibility and gene expression leakage.
Proceedings Article

A discrimination study of human core-promoters.

TL;DR: Using position-specific k-tuple feature variables, a quadratic discriminant analysis (QDA) method is shown to be very effective in identifying human core-promoters.