How does Kellis use computation in genomics?
Computation is fundamental to my approach; it's the lens through which we interpret biological complexity. We use machine learning to identify patterns in enormous genomic datasets, essentially training models to recognize functional elements or predict regulatory outcomes. Evolutionary analysis, powered by computation, allows us to compare genomes across species, highlighting conserved regions that are likely critical for function. This allows us to ask 'What is the algorithm?' behind a biological process, treating the genome as a program to be deciphered. It enables us to move from raw sequence data to a functional understanding, uncovering the underlying logic of life.
Ask Manolis Kellis the follow-up →