Anisha Luthra

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.

The paper investigates how natural genetic variation affects resistance to CRISPR gene drives in Drosophila, finding that resistance rates vary widely and are polygenic with small-effect loci.

This study evaluates strategies to reduce resistance allele formation in CRISPR gene drives, finding that using the nanos promoter, adding a second gRNA, and using an autosomal male germline drive can lower resistance rates.