Debyani Chakravarty

The paper uses machine learning to identify cancer driver mutations, finding that methods incorporating protein structure or functional genomic data outperform those using only evolutionary data, and validates their predictions using real-world patient cohorts.

This study presents a paired tumor:normal sequencing dataset for hematologic malignancies, showing improved identification of somatic variants and their clinical relevance.

The paper harmonizes clinical interpretations of cancer genomic variants across six knowledgebases, improving overlap and matching with patient cohorts.