Deep Learning for 3D Protein Structure Predictions – Giulia Crocioni on DeepRank2

Deep Learning for 3D Protein Structure Predictions – Giulia Crocioni on DeepRank2

#8: Giulia Crocioni joins Arfon and Abby to discuss DeepRank2, a deep learning framework for 3D protein structure predictions. Giulia is a Research Software Engineer at the Netherlands eScience Center where she uses different machine learning techniques to develop and contribute to methodologies and applications to answer life sciences research questions. You can follow Giulia on LinkedIn See the show notes: https://blog.joss.theoj.org/2024/04/josscast-8-deep-learning-for-3d-protein-structure-predictions


Episode highlights: - [00:02:16] Giulia introduces herself and her work at the Netherlands eScience Centre in the Life Sciences section. - [00:04:09] Introducing DeepRank2: An open-source deep learning framework for data mining of protein-protein interfaces or single-residue variants. - [00:10:14] Target audiences (researchers, developers) and applications (drug design, cancer vaccine development) - [00:13:48] Ecosystem overview from complementary projects like AlphaFold, to training data sources, to using PyTorch. - [00:23:27] Open Source & Contributions Links:

See the show notes: https://blog.joss.theoj.org/2024/04/josscast-8-deep-learning-for-3d-protein-structure-predictions


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Supercharge your research with the latest scientific software showcased in the Journal of Open Source Software (JOSS). Hear directly from authors on their work, their motivations, and new ways open source software can accelerate your work.

Hosted by editor-in-chief Arfon Smith and founding editor Abby Cabunoc Mayes, each episode features an interview with different authors of published papers in JOSS. Tune in to learn about the latest developments in research software engineering and open science, and how they are changing the way research is conducted.

New episodes every other Thursday.

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