Editor - Zhenqiu Liu | Epidemiology and Public Health, Universit | 348
ISSN: 2161-1165

Epidemiology: Open Access
Open Access

Like us on:

Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Zhenqiu Liu

Zhenqiu Liu
Zhenqiu Liu
Epidemiology and Public Health
University of Maryland
Tel: 410-706-8523


Dr. Zhenqiu Liu is an Associate Professor at the Department of Epidemiology and Public Health and Greenebaum Cancer Center University of Maryland at Baltimore. His research interests are in the broad area of statistical genetics bioinformatics and data mining. Currently his efforts are concentrated on the integrated analysis of human genomic and metagenomic data ROC based methods for biomarker evaluation and prediction efficient methods for cohort and observational data and statistical analysis for network and system biology. He has a MS in Computer Science and PhD in Management Science Operations Research with concentration in data mining both from the University of Tennessee at Knoxville UTK.

Research Interest

Statistic Genetics, Bioinformatics, and Data Mining: Currently I am working on projects in (1) ROC based methods for gene selection, pathway identification, and biomarker discovery, (2) GWAs, gene-gene interaction and genetic networks, and rare allele identification, (3) integrated analysis for multi-source genomic data, and (4) causal inference and biomarker identification and evaluation in clinical and translational cancer research. I am interested in both methodology and collaborative research and constantly looking for investigators who are willing to use cutting-edge machine learning and statistical computing techniques in their study.