GlaxoSmithKline is searching for new oncology drug targets. We have CRISPR knockout data for many cancer cell lines and many genes. For these same cell lines, we also have genomic data --somatic mutations, copy number variants, and gene expression. We use machine learning (random forests) to find predictive relationships between genomic features and cell line growth under knockout. Then we use GLASSES, a shiny app, to share the results with biologists. GLASSES lets scientists interactively explore key relationships and discover novel cancer vulnerabilities.
David Cooper
GlaxoSmithKline
Using Machine Learning and Interactive Graphics to Find New Cancer Targets
GlaxoSmithKline is searching for new oncology drug targets. We have CRISPR knockout data for many cancer cell lines and many genes. For these same cell lines, we also have genomic data --somatic mutations, copy number variants, and gene expression. We use machine learning (random forests) to find predictive relationships between genomic features and cell line growth under knockout. Then we use GLASSES, a shiny app, to share the results with biologists. GLASSES lets scientists interactively explore key relationships and discover novel cancer vulnerabilities.