Tongjun Gu, PhD
Dr. Gu’s research is at the forefront of developing and implementing advanced Artificial Intelligence algorithms and statistical models, with a particular emphasis on deep learning and machine learning techniques. Her dedication lies in unraveling the intricacies of complex biological systems and uncovering the underlying mechanisms of human diseases. With extensive training and expertise spanning genomics, genetics, epigenetics, genome-wide association studies, cancer biology, RNA sciences, and Alzheimer's disease, Dr. Gu is uniquely poised to tackle the challenges in these fields.
Leveraging her comprehensive background in various omics data analysis techniques, Dr. Gu's primary research centers on pioneering cutting-edge computational methodologies for the integration analysis of multi-omics data. Through this innovative approach, Dr. Gu aims to delve into the intricate interactions within these diverse datasets, pinpointing both personalized and generalized key elements that contribute to the onset of diseases. Her ultimate objective is to advance the field of early diagnosis and personalized treatment strategies, with a specific focus on cancers such as leukemia, lung cancer, and kidney cancer.
Furthermore, Dr. Gu's research extends to exploring the expanded functions of miRNA and RNA editing. She has dedicated over a decade to studying these aspects, particularly within disease contexts, employing a variety of computational approaches. Her multifaceted research efforts promise to shed light on critical aspects of molecular biology and their implications in disease mechanisms.
Open Researcher and Contributor ID (ORCID)
Selected Original Research Articles (*corresponding author)
Click here for a full list of publications from Tongjun Gu, PhD
H Palma‐Gudiel, L Yu, Z Huo, J Yang, Y Wang, T Gu, C Gao, PL De Jager, P Jin, DA Bennett, J Zhao. (2023) Fine-mapping and replication of EWAS loci harboring putative epigenetic alterations associated with AD neuropathology in a large collection of human brain tissue samples. Alzheimer's & Dementia, https://doi.org/10.1002/alz.12761.
T Gu*, M Xie, WB Barbazuk, JH Lee*. (2021) Biological features between miRNA and their targets are unveiled from deep learning models. Scientific Reports, 11 (1), 1-10.
T Gu*, X Zhao, WB Barbazuk, JH Lee. (2021) miTAR: a hybrid deep learning-based approach for predicting miRNA targets. BMC bioinformatics, 22 (1), 1-16.
CJ Fields, Lu Li, NM Hiers, T Li, P Sheng, T Huda, J Shan, L Gay, T Gu, J Bian, MS Kilberg, R Renne, M Xie. (2021) Sequencing of Argonaute-bound microRNA/mRNA hybrids reveals regulation of the unfolded protein response by microRNA-320a. PLoS Genetics, 17 (12), e1009934.
T Gu*, AQ Fu, MJ Bolt, X Zhao. (2020) Systematic identification of A-to-I editing associated regulators from multiple human cancers. Computers in Biology and Medicine, 119, 103690.
T Gu*, X Zhao*. (2019) Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders. Scientific reports, 9 (1), 1-11.
T Gu*, AQ Fu, MJ Bolt, KP White. (2019) Clinical Relevance of Noncoding Adenosine-to-Inosine RNA Editing in Multiple Human Cancers. JCO clinical cancer informatics, 3, 1-8.