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Zheng Li

Researcher at Tianjin University of Traditional Chinese Medicine

Publications -  131
Citations -  3496

Zheng Li is an academic researcher from Tianjin University of Traditional Chinese Medicine. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 21, co-authored 100 publications receiving 2560 citations. Previous affiliations of Zheng Li include Michigan State University & Boston University.

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A network pharmacology study of Chinese medicine QiShenYiQi to reveal its underlying multi-compound, multi-target, multi-pathway mode of action

TL;DR: This study depicts a complex MOA of QSYQ on myocardial infarction, a cardiovascular disease-related multilevel compound-target-pathway network connecting main compounds to those DEGs supported by literature evidences and the pathways that are functionally enriched in ArrayTrack.
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Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis

TL;DR: A new hybrid algorithm integrating ordinary differential equation models with dynamic Bayesian network analysis, called Differential Equation-based Local Dynamic Bayesian Network (DELDBN), was proposed and implemented for gene regulatory network inference and significantly improved the accuracy and sensitivity of network inference compared with other approaches.
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Transgenic insect resistance traits increase corn yield and yield stability.

TL;DR: The most widely tested corn hybrid, NB6016, had triple-stacked CB/ CRW/RR and RR (NB6016 RR) pairs grown together in 736 yield tests, and was higher yielding than the isogenic RR control in 77% of the tests, with an average yield advantage of 0.55 ± 0.81 tonnes/ha.
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Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization.

TL;DR: Modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision and performed better than single modified causal inference in predicting therapeutic drug-disease associations.