TRILL: Orchestrating Modular Deep-Learning Workflows for Democratized, Scalable Protein Analysis and Engineering: Revision history

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11 September 2024

27 December 2023

  • curprev 21:1421:14, 27 December 2023 Murray talk contribs 1,941 bytes +1,941 Created page with "{{Paper |Title=TRILL: Orchestrating Modular Deep-Learning Workflows for Democratized, Scalable Protein Analysis and Engineering |Authors=Zachary A Martinez, Richard M. Murray, Matt W. Thomson |Source=Submitted, 2024 SEED |Abstract=Deep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that le..."
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