Name

 Lida Zhu

Title

Assistant Professor

Institute

College of Informatics, Huazhong Agricultural   University

Address

No.1   Shizishan Street, Hongshan District, Wuhan 430070, China

Email

ldzhu@hzau.edu.cn

Fax

Research Interest

My main interests lie in the field of   bioinformatics and systems biology. My main research work focuses on the exploration   and research including complex diseases, non-coding RNA and network   pharmacology by developing and using complex networks, machine learning, deep   learning, graph theory, combinatorial mathematics and other methods, and has   made a series of important progress.

Research interests include pathogenic gene   recognition, prediction of microRNA disease relationship, prediction of disease-related   noncoding RNA environment factor combination, research of prediction of drug   target, drug combination, prediction of protein interaction, etc. These   research directions are the frontier and hot spot of cross research in   mathematics, computer science, life science and many other fields in recent   years, which have very important theoretical and practical significance.

Academic activities

I have served as a reviewer for Frontiers   in genetics、BMC Genomics、BMC Bioinformatics、IEEE Computational Biology and   Bioinformatics and for the funding branch of the NSFC (China).

Education and research experience

Grants (Government-funded)

Representative publications（*for corresponding authors, # for first authors）

(1) Quan Y, Luo Z, Yang Q, Li J, Zhu Q, Liu Y, Lv B, Cui Z, Qin X,   Xu Y, Zhu L*, Zhang H. Systems chemical genetics-based drug discovery: prioritizing   agents targeting multiple/reliable disease-associated genes as drug   candidates.29 May 2019. Frontiers in genetics. DOI: 10.3389/fgene.2019.00474

(2) Quan Y, Liu Y, Liu M, Wu Y, Zhu L,   Luo Z, et al. (2018). Facilitating anti-cancer combinatorial drug discovery   by targeting epistatic disease genes. Molecules, 23(4), 736.

(3) Zhu L#, Zhu F.   Identification association of drug-disease by using functional gene module   for breast cancer[J]. Bmc Medical Genomics, 2015, 8(S2):1-8.

(4) Zhu L#, Liu J. Integration of a prognostic gene module with a drug   sensitivity module to identify drugs that could be repurposed for breast   cancer therapy.[J]. Computers in Biology & Medicine, 2015, 61(C):163.

(5) Zhu L#, Liu J, Liang F, et al. Predicting response to preoperative   chemotherapy agents by identifying drug action on modeled microRNA regulation   networks[J]. Plos One, 2014, 9(5):e98140.

(6) Wang W, Liu J, Xiong Y, Zhu L, et al. Analysis and   classification of DNA-binding sites in single-stranded and double-stranded   DNA-binding proteins using protein information[J]. Iet Systems Biology, 2014,   8(4):176.

(7) Zhu L#, Li J. Water Bioinformatics: An Association between Estrogen   Degradation and 16S rRNA Motifs, International Conference on Bioinformatics   and Biomedical Engineering. IEEE, 2010:1-4..

(8) Zhu L#, Liang F, Liu J, et al. Dynamic remodeling of context-specific   miRNAs regulation networks facilitate in silico cancer drug screening, IEEE   International Conference on Systems Biology. IEEE, 2011:292-302..

(9) Zhu L#, He C, Liu Y, et al. A systems chemical biology approach to   identify targets of antibacterial agents: A case study of Chelerythrine and   Rhein, IEEE International Conference on Bioinformatics and Biomedicine. IEEE,   2015:1047-1056.

(10) Zhu L#, Yuan J. (2019). Predicting Potential Drug-Target Interactions   with Multi-label Learning and Ensemble Learning.   10.1007/978-3-030-26969-2_69.