Zhang Lab of Artificial Intelligence in Medicine

Division of Artificial Intelligence in Medicine at Cedars-Sinai. 6500 Wilshire Blvd, Los Angeles, CA 90048.

about/cedars-sinai-skybridge.jpg
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.

Recent News
(see all)

Aug 23, 2024 Prof Zhang Will Move And Close His Lab
Dec 15, 2023 Elektrum Published At Nature Computational Science
Nov 2, 2023 We are honored to receive the 2023 Winnick Award!
Oct 27, 2023 Three Papers Accepted At Neurips23 Workshops
Oct 19, 2023 Emgnn Paper Accepted At Bioinformatics

Selected Publications

  1. Nat. Comput. Sci.
    Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions
    Zhang, Zijun, Lamson, Adam R, Shelley, Michael, and Troyanskaya, Olga
    Nature Computational Science 2023
    HTML Code
  2. 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
    HTML Code
  3. 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
    HTML Code