Deep Learning has been revolutionalizing many industries since its inception, including proteomics (the science of proteins) as epitomized quite recently by DeepMind's AlphaFold. We will discuss the role of deep neural network models in proteomic data analyses. More specifically, we will present how we trained a multi-task model to speed up inference and how we approached deploying deep learning models in Windows desktop applications.

Learn about the specific challenges of applying deep neural network models in such a context, how to keep them up-to-date and how to prepare training data in the industry.

Speakers:
Timothy Man and Lucie Piecková

Tim is a Senior Software Engineer for Biognosys AG in Schlieren. As a physicist in a team of bioinformaticians and a company of biologists, he's the odd one out. One of his tasks is to implement deep learning models into the different software suites of Biognosys.

Lucie, during her research journey, replaced a proteomics wet lab for an in silico one and teamed up with Tim to develop deep learning models at Biognosys. She is a Data Science graduate from the Propulsion Academy and a genuine Unicorn (graduate of Unicorn University in Prague).

Sponsored by SAS

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