Welcome to the Zhang Lab of Artificial Intelligence in Medicine!
Our lab uses statistical learning and deep learning as the unifying language to communicate across research areas in computational biology. Currently the primary research interests include:
Automated Deep Learning (AutoDL)-powered Interpretation of Genetic Variations
We develop AutoDL techniques to accommodate the rapid biotechnology development with modern deep learning. These automated, data-driven models help us understand the causal effects of genetic variations in various contexts, including epigenetics, transcription, genome editing, and in tumors. In the long term, we hope our methods will democratize deep learning and thereby accelerate scientific discoveries in biomedicine.
Post-transcriptional and Transcriptional Regulatory Network
We also study the molecular mechanisms that mediate the genetic effects to phenotypical variations and disease prognostics. Enabled by high-throughput sequencing technologies, we characterize the variation of RNA expression, RNA splicing, RNA-protein interactions, and RNA modifications in health and disease by quantitative, statistical and deep learning approaches.
|Jul 10, 2023||Huixin Zhan joined us as a Postdoctoral Scientist. Welcome, Huixin!|
|Mar 3, 2023||Covid Splicing Study Featured On Cover Of Cell Reports Methods|
|Feb 20, 2023||Tinghui Wu joined us as a Data Scientist. Welcome, Tinghui!|
|Nov 9, 2022||Justin Ling joined us as a Data Scientist. Welcome, Justin!|
|Nov 1, 2022||Victoria Li Awarded The 2021 Milton Fisher Scholarship|
Nat. Mach. Intell.An automated framework for efficiently designing deep convolutional neural networks in genomicsNature Machine Intelligence 2021
Nat. MethodsDeep-learning augmented RNA-seq analysis of transcript splicingNature methods 2019