Center for Statistics in Big Data (CSBD)
Advancing statistical and computational methods to transform high-dimensional multi-modal biomedical data into actionable insights for precision health.
CSBD Resources
The Center provides a broad range of resources to support biomedical data science research across the University of Pennsylvania. Through methodological expertise, collaborative partnerships, educational programs, and shared computational resources, the Center aims to accelerate scientific discovery and advance precision health.
Research Resources
- Statistical and AI methods for biomedical and population health data
- Open-source software and reproducible workflows
- Access to high-performance computing and cloud-based platforms
- Data resources spanning genomics, single-cell and spatial omics, microbiome and virome studies, electronic health records, imaging, and wearable devices
Training and Community
- Penn Conference on Big Data in Biomedical and Population Health Sciences
Annual conference bringing together researchers to explore advances in big data analytics, machine learning, and data-driven healthcare. - Research Integration Focused Series (DBEI-RISE)
- Graduate and postdoctoral training opportunities
- Student mentoring and interdisciplinary collaboration programs
Collaborative Research
The Center for Statistics in Big Data actively partners with investigators across Penn Medicine, CHOP, and external institutions to develop innovative methods and address important biomedical challenges.
Biomedical Data Science Core
The Biomedical Data Science Core (BDSC) provides collaborative statistical and computational support for biomedical investigators. Services include study design, statistical analysis, machine learning, multi-omics integration, clinical data analysis, and development of customized analytic pipelines.
Please contact us to discuss potential collaborations or to learn more about BDSC services.
Software, Data Resources, and GitHub Repositories
RCANE
A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Abberation Prediction using RNA-seq Data
GitHub Link: https://github.com/HowardGech/RCANE