AI


Has Computational Modeling for Cancer Genomics "Arrived?" with Shirley Liu, Dana Farber

It’s the question of the moment Are we living in the age of AI? Or is it still just hype?

When it comes to the latest research in immuno therapy, computational modeling is helping to answer key open questions, such as which patients might respond to which drugs.

"If you were to ask me last year about deep learning, I would probably say, aaah, most of the algorithms that are published are not really answering the important questions yet. But I think this year I am converted. We are starting to use deep learning, and we are starting to see interesting results.”

Shirley Liu is a Professor of Biostatistics at Harvard and the Co-Director of the Center for Functional Cancer Epigenetics at the Dana Farber Cancer Institute. Her lab has very recently put out three algorithms, TRUST, TIMER, and TIDE which represent some very exciting ways that bioinformatics is empowering not only cancer research but treatment decisions.

As a computational biologist, Shirley has found herself highly in demand today as the latest genomic tools such as single cell sequencing generate new amounts of data and as public databases such as TCGA make their rich cohorts available.

In today’s interview, she details these three new algorithms and makes the case that computational modeling has arrived for cancer genomics.



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