J
Jianyang Zeng
Researcher at Tsinghua University
Publications - 101
Citations - 3620
Jianyang Zeng is an academic researcher from Tsinghua University. The author has contributed to research in topics: Deep learning & Ribosome profiling. The author has an hindex of 23, co-authored 100 publications receiving 2390 citations. Previous affiliations of Jianyang Zeng include Duke University & Nanyang Technological University.
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Journal ArticleDOI
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
Yunan Luo,Xinbin Zhao,Jingtian Zhou,Jinglin Yang,Yanqing Zhang,Wenhua Kuang,Jian Peng,Ligong Chen,Ligong Chen,Jianyang Zeng +9 more
TL;DR: DTINet is introduced, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations, which accurately explains the topological properties of individual nodes in the heterogeneous network.
Journal ArticleDOI
Allelic reprogramming of 3D chromatin architecture during early mammalian development
Zhenhai Du,Hui Zheng,Bo Huang,Rui Ma,Jingyi Wu,Xianglin Zhang,Jing He,Yunlong Xiang,Qiujun Wang,Yuanyuan Li,Jing Ma,Xu Zhang,Ke Zhang,Yang Wang,Michael Q. Zhang,Juntao Gao,Jesse R. Dixon,Xiaowo Wang,Jianyang Zeng,Wei Xie +19 more
TL;DR: The data suggest that chromatin may exist in a markedly relaxed state after fertilization, followed by progressive maturation of higher-order chromatin architecture during early development, as characterized by slow consolidation of TADs and segregation of chromatin compartments in preimplantation development.
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Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach
TL;DR: Tests show that the multimodal DBN based data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data.
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A deep learning framework for modeling structural features of RNA-binding protein targets
TL;DR: A general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time and provides a new evidence to support the view that RBPs may own specific tertiaryStructural binding preferences.
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Predicting drug-target interactions using restricted Boltzmann machines
Yuhao Wang,Jianyang Zeng +1 more
TL;DR: A first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action is proposed, which indicates that the approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process.