Manually Curated Proteins


Predicted Proteins

Welcome to Guomics


We are a multidisciplinary team from diverse background.


We develop reproducible, fast, deep and low-cost proteomics technologies.


We believe quantitative biological rules are hidden in proteomic big data.


Big data and artificial intelligence empower knowledge mining.

Research Interests

Our group are interested in developing cutting edge proteomics technologies to precisely quantify maximum number of proteins from the minimum amount of biological or clinical samples with the maximum sample throughput, enabling the generation of proteomic big data research to tackle complex biomedical questions.

The base technology we are using is the PCT-SWATH/DIA (Guo, et al, 2015), the abbreviation for pressure cycling technology and sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (protected term from SCIEX) and data-independent acquisition (DIA, more generic description)

In Westlake, we continue to develop new technologies for generating proteomic big data with higher throughput, reproducibility and cheaper costs (Sun et al, 2020; Gao, et al, 2020; Cai, et al. 2020)

We are also developing computational resources to democratize the data analysis of proteomic big data sets (Zhu, et al. 2019; Zhu, et al. 2020)

Our research relies on equipments including barocyclers, mass spectrometers and high performance computing systems.

We developed PCT-SWATH/DIA technology which allows fast, reproducible and cheap proteomic analysis of tissue specimens including fresh tissues and formalin-fixed paraffin-embedded (FFPE) tissues. With no more than 1 mg tissue, the PCT method produces on average 50 µg peptides, while only 0.2-1µg peptides are sufficient for an optimal generation of a SWATH/DIA map using TripleTOF (Shao, et al. 2015) or the latest Orbitraps such as Q-Exactive HF/HFX, Fusion and Lumos within 0.5-2 hrs. We also worked with Pressure Biosciences Inc in developing PCT-MicroPestle for enhanced sample preparation efficiency (Shao, et al. 2016).

The long-term goal of our group is to build up digital biobanks of proteomics of clinical specimens, and develop proteomic big data, cloud computing and AI based new type of diagnostic methods.

The proteomic big data technologies are applied to stratify intermediate prostate cancers.

Prostate cancers are the most common cancers in men. It’s highly prevalent all over the world and largely over-diagnosed and over-treated.

We are also working on developing protein and deep learning based diagnostic tools for thyroid cancer.

Thyroid cancers are the most common cancers in women. It’s highly prevalent all over the world and largely over-diagnosed and over-treated.

We have several openings for postdoc scholars. Please contact the PI with a statement letter of research interest if you are interested.