cancer.silver.is
Computational approaches to cancer research. This site collects papers, guides, and eventually tools for bioinformaticians, systems engineers, and anyone who wants to contribute to the fight against cancer but doesn't know where to start.
The work here is opinionated: cancer's most consequential challenges are infrastructural, not algorithmic. The data exists. The models work. What's missing is the plumbing that connects them to patients.
Papers
Cancer Bioinformatics
A Quickstart for Computational People
The minimum viable knowledge to begin contributing to cancer bioinformatics: what cancer is, how it is classified, how it is treated, where computation enters, what the key datasets are, what the open problems are, and where to start. Opinionated. Assumes you learn by building.
Computational Approaches to Cancer
A Literature Review for Bioinformaticians, Systems Engineers, and the Impatient
A survey of computational cancer research across eight domains: genomic variant interpretation, single-cell and spatial transcriptomics, digital pathology, protein structure and drug discovery, liquid biopsy and early detection, large language models in clinical oncology, laboratory automation, and data equity. The field's most consequential challenges are no longer primarily algorithmic but infrastructural.
Coming soon
Guides for getting started with cancer bioinformatics tooling. Dataset index. Lab automation notes. Collaboration coordination.
For Ariel and Bea.