Posters presented by lab members: Chenyang Yuan: Integration of label-free, interpretable image features with spatial molecular profiles.(PB5093) Yefeng Yang: Efficient quantification of pattern similarity between spatial genomics and cell morphology. (PB3369)

The meeting will include presentations from renowned speakers on recent developments and applications of deep learning models in bioinformatics, genomics, and structural biology, as well as interactive workshops and networking. Link: 2023 IOB Symposium

Congratulations on our groundbreaking collaborative publication in Nature Medicine: “Pan-cancer T cell atlas reveals crucial cellular stress response state associated with immunotherapy resistance.” In this remarkable study, we have successfully identified a unique stress response state, TSTR, characterized by heat shock gene expression. TSTR cells are detectable in situ in the tumor microenvironment across various […]

Glad to share our latest publication in Cell Systems. We developed a method named TESLA, which is a spatially-resolved transcriptomics data analysis tool for integrates gene expression and histology to annotate tumor microenvironment at super-resolution. Link: https://doi.org/10.1016/j.cels.2023.03.008

Hu received the Kaushal multidisciplinary science publication award 2023 at the Genetics annual Symposiumre at Perelman School of Medicine, University of Pennsylvania.

JOB DESCRIPTION The AI in Genomics Lab at the Department of Human Genetics at Emory University (PI: Jian Hu, PhD) invites applications for a postdoctoral fellow position in support of research in Statistical Genomics and specifically in the areas of developing statistical and machine learning algorithms to decipher disease mechanism. The initial appointment is for […]

Dr. Hu’s paper on spatial transcriptomics data analysis won the 2021 ASA Section of Statistics in Genomics and Genetics best student paper award! Dr. Hu will present his work at JSM in August.

Novel Machine Learning for Genomics Data in course Statistical Machine Learning for Data Science This course provides learning opportunities on statistical software, R, with some focus on data management and wrangling, reproducibility, and visualization. On top of that, there are basic introductions to Machine Learning such as k-NN, Naive Bayes, regression methods, etc. The focus […]

This course introduces basic principles of genetic and molecular epidemiology through interactive discussion with leading researchers in the field. This is a stand-alone course but is also a prerequisite for the Genetic and Molecular Epidemiology Certificate Program.

Deciphering tissue microenvironment from Next Generation Sequencing data The advent of high-throughput next-generation sequencing (NGS) technologies has transformed our understanding of cell biology and human disease. As NGS has been adopted earliest by the scientific community, its use has now become widespread, and the technology has improved rapidly. At present, it is now common for […]