Resources
Our Software and Tools
ecPath
A deep learning approach to detect ecDNA in tumors from histopathology images, making previously expensive analyses accessible through routine clinical data.
Machine Learning Resources
Our lab develops and curates computational resources to advance cancer and aging research. Here are some key resources:
Big Data Resources in Cancer Research
We've compiled a comprehensive list of datasets, tools, and resources for researchers studying cancer. These resources span genomics, transcriptomics, proteomics, and imaging data repositories.
View Resources →Computational Methods for Spatial Transcriptomics
Spatial transcriptomics is revolutionizing our understanding of tissue biology. We've assembled guides and tools to help researchers get started with spatial transcriptomics techniques.
Learn More →Computational Methods to Probe the Immune System
The immune system plays a critical role in cancer. We've created a collection of computational methods specifically designed for analyzing immune system dynamics.
Explore Methods →Machine Learning for Biomedical Research
We've developed a guide to help researchers learn machine learning for a starter.
View ML Resources →Open Questions in Caner and Aging Research
We've identified key unsolved questions at the intersection of aging and cancer that represent exciting opportunities for computational biologists.
Explore Open Questions →Contact Us
Interested in collaborating or need assistance with any of these resources? Contact us to discuss potential research partnerships or technical support.