Zhang Lab of Artificial Intelligence in Medicine

Division of Artificial Intelligence in Medicine at Cedars-Sinai. 116 N. Robertson Blvd. Los Angeles, CA 90048.

Pavilion, Cedars-Sinai Medical Center

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:

  1. 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.

  2. 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

selected publications

  1. Nat. Mach. Intell.
    An automated framework for efficiently designing deep convolutional neural networks in genomics
    Zhang, Zijun, Park, Christopher Y, Theesfeld, Chandra L, and Troyanskaya, Olga G
    Nature Machine Intelligence 2021
  2. Nat. Methods
    Deep-learning augmented RNA-seq analysis of transcript splicing
    Zhang, Zijun, Pan, Zhicheng, Ying, Yi, Xie, Zhijie, Adhikari, Samir, Phillips, John, Carstens, Russ P, Black, Douglas L, Wu, Yingnian, and Xing, Yi
    Nature methods 2019