Protein Structure Deep Learning with Mohammed Al Quraishi

RECENT UPDATES:

Podsheets is our open source set of tools for managing podcasts and podcast businesses

New version of Software Daily, our app and ad-free subscription service

Software Daily is looking for help with Android engineering, QA, machine learning, and more

FindCollabs Hackathon has ended–winners will probably be announced by the time this episode airs; we will be announcing our next hackathon in a few weeks, so stay tuned

Until Google DeepMind came into the field, protein structure prediction was dominated by academics.

Protein structure prediction is the process of predicting how a protein will fold by looking at genetic code. Protein structure prediction is a perfect field to approach through the application of deep learning, because the inputs are highly dimensional and there is a plentiful array of different sets of labeled data. Protein structure deep learning is a field in which many different approaches are taken, often involving supervised learning and reinforcement learning.

Mohammed Al Quraishi is a systems biologist at Harvard. His background spans computer engineering, statistics, and genetics. In his work, Mohammed explores the interplay between biology and computer systems.

One area of Mohammed’s focus is protein structure prediction. In a blog post last year, Mohammed gave a brief history of protein structure prediction and described the significance of DeepMind entering the field. DeepMind’s AlphaFold technology surpassed all other competitors in the most recent CASP protein structure competition.

Mohammed joins the show to discuss biology, academia, deep learning, and DeepMind.

The post Protein Structure Deep Learning with Mohammed Al Quraishi appeared first on Software Engineering Daily.