Department of Biomedical Engineering
Azusa Pacific University, USA
Wenlong Tang is a postdoctoral fellow in the Department of Biomedical Engineering at Tulane University. His research interests include data integration and statistical modeling in high-throughput and high-dimensional data space, such as genomic data and gait data. He developed a noninvasive early detection methodology based on locomotion analysis for neurological and neuromuscular disorders. He has co-authored six published journal papers and is also the co-inventor of a pending US patent and two Chinese patents. He received his Ph.D. in Mechanical Engineering from University of Maryland Baltimore County in 2010, M.S. degree in Beijing University of Technology in 2006 and Bachelor’s degree in Beijing University of Aeronautics and Astronautics, Beijing, China in 2004. He is a member of IEEE and SfN.
Develop data integration approaches for analyzing the next generation sequencing data (NGS), single-nucleotide polymorphism (SNP) data, gene expression data and gene copy number data related to cancers.
genomic data has already been generated and continues to be generated
detecting the presence of neurological and neuromuscular disorders at an early stage. To detect those disorders using non-invasive techniques and with low bias is extremely difficult right now. detecting neurological and neuromuscular diseases in an early stage by using an in-house gait analysis system in laboratory rats in a non-invasive way. analyze the data in common computer languages, such as C++, Java, etc. They can also program by using some commercial software packages, such as LabVIEW, MATLAB.