CanPredict -- a computational tool for predicting cancer-associated missense mutations
What you can do:
Identify mutations most likely to be cancer-associated.
Highlights:
- We previously developed an algorithm, and now present the web application, CanPredict (see also http://www.cgl.ucsf.edu/Research/genentech/canpredict/), to allow users to determine if particular changes are likely to be cancer-associated.
- The impact of each change is measured using two known methods: Sorting Intolerant From Tolerant (SIFT) and the Pfam-based LogR.E-value metric.
- A third method, the Gene Ontology Similarity Score (GOSS), provides an indication of how closely the gene in which the variant resides resembles other known cancer-causing genes.
- Scores from these three algorithms are analyzed by a random forest classifier which then predicts whether a change is likely to be cancer-associated.
Keywords:
- Genetic Predisposition to Disease
- Missense Mutation
- Neoplasms
- cancer
- mutation
- Gene Ontology Similarity Score
Literature & Tutorials:
This record last updated: 06-02-2008