New CRISPR Protein and Genome Editing Techniques Using Machine Learning Techniques with Hannah Spinner

How can the application of machine learning make CRISPR even more beneficial than it already is? By lowering bench time, researchers may free time to find even more beneficial advancements. Listen in to learn:

• The potential concerns some researchers pose
• How domains can serve multiple functions
• The function of fetal hemoglobin

Hannah Spinner, a research specialist and Ph.D. candidate at Harvard, discusses her work in applying machine learning on CRISPR technologies and new CRISPR proteins.

Applying machine learning techniques that have been proven to advance technologies in other fields holds promising results in increasing the efficiency of CRISPR technology. Reducing the tedious lab work required by researchers will allow new advancements in how we interact during the research process.

New advancements and discoveries regarding proteins and their use in CRISPR have opened the possibilities of adding a function that was not previously available. By editing various bases on the genome, the function of CRISPR in that area has a wide array of possibilities.

To learn more, search for Hannah Spinner on Twitter at @bellespinner.

Episode also available on Apple Podcasts: apple.co/30PvU9C