Zejun Li
Hunan University
5 Papers
18 Citations
Zejun Li is an academic researcher from Hunan University. The author has contributed to research in topics: Cancer & Thyroid cancer. The author has an hindex of 4, co-authored 5 publications. Previous affiliations of Zejun Li include Hunan Institute of Technology.
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Papers
Revealing Drug-Target Interactions with Computational Models and Algorithms
Liqian Zhou,Zejun Li,Jialiang Yang,Geng Tian,Fuxing Liu,Hong Wen,Li Peng,Min Chen,Ju Xiang,Ju Xiang,Lihong Peng +10 more
TL;DR: This work provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods, including bipartite local model, matrix factorization, regularized least squares, and deep learning.
51
Molecular Network-Based Drug Prediction in Thyroid Cancer.
Xingyu Xu,Haixia Long,Baohang Xi,Binbin Ji,Zejun Li,Yunyue Dang,Caiying Jiang,Yuhua Yao,Jialiang Yang +8 more
TL;DR: It is inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells, and drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancers are predicted.
19
Identifying human microRNA-disease associations by a new diffusion-based method.
TL;DR: A new diffusion-based method (NDBM) to explore global network similarity for miRNA-disease association inference and some associations who strongly predicted by the method are confirmed by public databases, suggesting that NDBM could be an effective and important tool for biomedical research.
16
SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction
TL;DR: A new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP) is presented, suggesting the excellent performance of the SRMDAP in predicting miRNAs and diseases.
Global network random walk for predicting potential human lncRNA-disease associations
TL;DR: A global network random walk model for predicting lncRNA-disease associations (GrwLDA) is developed to reveal the potential associations between lncRNAs and diseases and significantly outperforming previous methods.