DNA methylation-based machine classification of cancer

Division Of Basic Sciences - Nci

Investigators

Linked publications, trials & patents

Abstract

My lab has continually developed novel methylation bioinformatic pipelines and machine learning-based classifiers for the classification of new CNS cancer types. Furthermore, this has recently expanded to extra-CNS cancer types through collaborative efforts. Using publicly-available data (DNA methylation, copy number, RNA-seq) and ongoing transcriptome sequencing through the LP, I am working to identify pathways through novel integrated bioinformatic techniques. I will computationally integrate these independent data types in order to identify new tumor types/subtypes and potentially refine current classification schemes; this approach will likely reveal functionally-relevant subtypes since DNA methylation, gene transcription, and chromatin accessibility are tightly linked. In order to achieve this, I will apply novel integrative techniques such as density-aware spectral clustering, similarity network fusion (SNF), and Bayesian consensus clustering. This approach will also serve to identify latent factors that drive the variability in the data and reveal potentially targetable pathways.