Abstract
Cancer results from an evolutionary process that yields a heterogeneous tumor with distinct subpopulations and varying sets of somatic mutations. This perspective discusses computational methods to infer models of evolutionary processes in cancer that aim to improve our understanding of tumorigenesis and ultimately enhance current clinical practice.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 397-401 |
| Number of pages | 5 |
| Journal | Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing |
| Volume | 27 |
| State | Published - Jan 1 2022 |
ASJC Scopus subject areas
- Biomedical Engineering
- Computational Theory and Mathematics