Center for Data Science and Artificial Intelligence
From Data to Discovery
Unlocking the Power of Biomedical Information
The future of biomedical research depends on our ability to make sense of vast, complex datasets. At Sanford Burnham Prebys, the Center for Data Science and Artificial Intelligence brings together experts in AI, statistics, genetics and more to uncover patterns, generate insights and spark innovation. Their work turns raw information into knowledge that drives scientific breakthroughs and new possibilities for human health.
“To drive groundbreaking discoveries in biomedical research by harnessing the power of data science and artificial intelligence, transforming how we diagnose, treat, and prevent disease.
We achieve this mission by data-centric research, inter-disciplinary collaborations, development and sharing of reusable resources, and contemporary training.”
We build a precision oncology computational approach capitalizes on recently published matched bulk and single-cell (SC) transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients’ SC tumor transcriptomics. The general objective of this project is to utilize single-cell omics from patients tumor to predict response and resistance. The following figure describe the architecture of PERCEPTION pipeline.
This portal provides a comprehensive catalog of circular extrachromosomal DNA (ecDNA) associated with childhood cancers, facilitating research and clinical insights. Explore detailed ecDNA profiles, patient information, and access valuable resources to advance scientific understanding and improve patient outcomes.
We build a novel framework, SAKURA, that uses knowledge-derived genes of interest to guide dimensionality reduction for scRNA-seq or scATAC-seq data, which can help separate highly similar cell subpopulations and detect rare cells (e.g. senescent cells).
PERsonalized single-Cell Expression-based Planning for Treatments In ONcology
We build a precision oncology computational approach capitalizes on recently published matched bulk and single-cell (SC) transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients’ SC tumor transcriptomics. The general objective of this project is to utilize single-cell omics from patients tumor to predict response and resistance. The following figure describe the architecture of PERCEPTION pipeline.
Childhood Cancer Catalog of Circular Extrachromosomal DNA
This portal provides a comprehensive catalog of circular extrachromosomal DNA (ecDNA) associated with childhood cancers, facilitating research and clinical insights. Explore detailed ecDNA profiles, patient information, and access valuable resources to advance scientific understanding and improve patient outcomes.
SAKURA: a knowledge-guided approach to recovering important, rare signals from single-cell data
We build a novel framework, SAKURA, that uses knowledge-derived genes of interest to guide dimensionality reduction for scRNA-seq or scATAC-seq data, which can help separate highly similar cell subpopulations and detect rare cells (e.g. senescent cells).
Sanford Burnham Prebys scientists say that understanding the potential pitfalls of using artificial intelligence and computational biology techniques in biomedical…
Mar 17, 2026
Mar 16, 2026
Feb 9, 2026
Nov 14, 2025
Nov 7, 2025
Oct 22, 2025
