Computational Biology Program

Through our Computational Biology Program, the QSU is utilizing cutting-edge computational and statistical models to analyze complex biological data, inform drug discovery, and advance the field of medicine—making a significant impact at both the institutional and national levels. The Computational Biology Program is centered around designing studies, analyzing data, and interpreting results for studies with data generated from high-throughput computational technologies. Our expertise includes bulk and single-cell RNA-Seq, genomics, proteomics, spatial transcriptomics, and other -omics data.

Our greatest impact is to collaborate with investigators by analyzing large, high-dimensional datasets that require specialized bioinformatics skills. These data often come from patient samples or wet lab experiments and encompass genomics (microarray imputation, exome/genome - short and long read sequencing), transcriptomics (bulk, single-cell, spatial), and proteomics (MassSpec, Somalogic, Olink). We integrate diverse datasets from genomics, transcriptomics, and proteomics to gain a holistic understanding of biological processes. This multi-omics approach enables us to create models that predict disease progression, therapeutic response, and patient outcomes more accurately.

The Quantitative Sciences Unit has a longstanding experience in biostatistics, data management, and reproducibility, particularly in clinical trials and other studies with fewer variables. In the Computational Biology Program, we analyze datasets containing millions of variables necessitating advanced bioinformatics expertise. Our team excels in handling diverse data types, including genomics, proteomics, transcriptomics, and other ‘omics. Each of these data types can contain thousands of variables that vary based on individual backgrounds and environmental exposures. Please reach out to Yann Le Guen at yleguen@stanford.edu if you would like to collaborate with the computational biology program at the QSU.

Program is using AI and machine learning to enhance computational biology models, improving their predictive power and accelerating scientific discovery. By leveraging AI and machine learning to enhance our models, these AI-driven insights are helping us identify new biomarkers and therapeutic targets more efficiently.

Please reach out to Yann Le Guen at yleguen@stanford.edu if you would like to collaborate with the QSU Computational Biology Program.