Haiyuan Yu
Assistant Professor
Haiyuan Yu


Fax: 607-255-5961


Department of Biological Statistics and Computational Biology
335 Weill Hall
Cornell University
Ithaca, NY 14853


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Department Profile

Lab Website

Research Description

We do research in the broad area of Biomedical Systems Biology with both high-throughput experimental (see Yu et al Science 2008) and integrative computational (see Yu et al PNAS 2006) methodologies, aiming to understand gene functions and their relationships within complex molecular networks and how perturbations to such systems may lead to various human diseases. The complexity of biological systems calls for building experimentally-verified computational models based on high-quality large-scale datasets, which is truly the future of biomedical research and the main theme of the lab. Our research are focused in five main areas:

Functional and Comparative Genomics
Integrating information from DNA sequences, gene expression, protein structures and other functional genomics datasets to elucidate gene functions, to understand network topology and its evolution, and finally to combine all of these knowledge and techniques into making accurate prognosis for various diseases, especially cancer.

Molecular and Dynamic Proteomics
Generating and analyzing genome-wide protein interactome (protein-protein and protein-DNA, in particular) maps for various organisms both computationally (with topological analysis and machine-learning approaches) and experimentally (with high-throughput Y2H, PCA, wNAPPA, and LUMIER assays); Investigating the dynamics of these networks upon perturbation (genetic variation, disease mutation, viral infection, environmental stress, etc).

Structural Genomics and Simulations
Relating three-dimensional protein structural information to current protein networks to better understand the role of each protein in the network, their relationships and dynamics upon perturbation.

Algorithms and Tools
To facilitate various research projects in the lab, we will develop new algorithms and tools to analyze different genomic and proteomic datasets. In particular, we are devoted to implement stand-alone and/or web-based tools that are easy to use by experimental biologists.

Technology Development
With the rapid advance in biotechnology, we will continue to implement, improve and develop cutting-edge high-throughput experimental methods with better accuracy and higher coverage.


  • Vo, T.V., et al. A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human. Cell 164, 310-323 (2016).
  • 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68-74 (2015).
  • Wei, X., et al. A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations. PLoS Genetics 10, e1004819 (2014).
  • Khurana, E., et al. Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 342, 1235587 (2013).
  • Guo, Y., et al. Dissecting Disease Inheritance Modes in a Three-Dimensional Protein Network Challenges the "Guilt-by-Association" Principle. American Journal of Human Genetics 93, 78-89 (2013).
  • Das, J., et al. Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways. Science Signaling 6, ra38 (2013).
  • Wang, X., et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nature Biotechnology 30, 159-164 (2012).
  • Yu, H., et al. Next-generation sequencing to generate interactome datasets. Nature Methods 8, 478-480 (2011).
  • Yu, H., et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104-110 (2008).