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Yuming Zhao
Researcher at Northeast Forestry University
Publications - 9
Citations - 262
Yuming Zhao is an academic researcher from Northeast Forestry University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 4, co-authored 4 publications receiving 234 citations. Previous affiliations of Yuming Zhao include Indiana University & Harbin Institute of Technology.
Papers
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Journal ArticleDOI
Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells
Guohua Wang,Guohua Wang,Yadong Wang,Yadong Wang,Weixing Feng,Weixing Feng,Xin Wang,Xin Wang,Jack Y. Yang,Yuming Zhao,Yuming Zhao,Yue Wang,Yunlong Liu +12 more
TL;DR: The proposed model-based approach indicates that considering combinatorial effects of transcription factors and microRNAs in a unified model provides additional transcriptional and post-transcriptional regulatory mechanisms on global gene expression in the prostate cancer with different hormone-dependency.
Journal ArticleDOI
BinMemPredict: a Web Server and Software for Predicting Membrane Protein Types
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A novel machine learning method for cytokine-receptor interaction prediction.
TL;DR: A novel machine-learning-based method for predicting cytokine-receptor interactions that is based on the k-skip-n-gram model, physicochemical properties, and local pseudo position-specific score matrix (local PsePSSM).
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A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer
Changyu Shen,Yi-Wen Huang,Yunlong Liu,Guohua Wang,Guohua Wang,Yuming Zhao,Yuming Zhao,Zhiping Wang,Mingxiang Teng,Mingxiang Teng,Yadong Wang,David A. Flockhart,Todd C. Skaar,Pearlly S. Yan,Kenneth P. Nephew,Tim H M Huang,Lang Li +16 more
TL;DR: A number of estrogen regulated target genes are identified and an established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor is established.
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Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm
TL;DR: This paper uses six feature extraction methods to extract sequence features from the same group of DBPs and uses the extreme gradient boosting (XGBoost) model to construct an effective predictive model.