Welcome to the home page for the Yu Lab at Cornell University! We perform research in the broad area of Network Systems Biology with both high-throughput experimental (see Vo et al., Cell 2016) and integrative computational (see Chen et al., Nature Genetics 2018) 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 is 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 function, understand network topology and evolution, and ultimately to combine all of these insights and techniques to make accurate prognoses for cancer and other diseases.

Molecular and Dynamic Proteomics

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

Structural Proteomics and Networks

Combining three-dimensional protein structural information with 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 actively develop new algorithms and tools to analyze different genomic and proteomic datasets. In particular, we are devoted to implementing stand-alone and web-based tools that are easy to use by experimental biologists.

Technology Development

With the rapid advance of biotechnology, we strive to implement, improve, and develop cutting-edge high-throughput experimental methods, especially novel cross-linking mass-spectrometry (XL-MS) technologies, with better accuracy and higher coverage.


FissionNet is a proteome-wide binary interactome network for the Fission Yeast S. pombe (Vo et al., Cell 2016).

MaXLinker is a software designed to identify cross-links from cross-linking massspectrometry (XL-MS) data.

INSIDER is a structural interactome browser for genomic studies.

HINT (High-quality interactomes) is a database of high-quality protein-protein interactions in different organisms.