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Updated: May 9, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Machine Learning for Protein Science and Engineering
Peter K Koo1, Christian Dallago2, Ananthan Nambiar3
1Simons Center for Quantitative Biology, Cancer Center Member, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA koo@cshl.edu christian.dallago@duke.edu nambiar4@illinois.edu yang.kevin@microsoft.com.
None:
Recent years have seen significant breakthroughs at the intersection of machine learning and protein science. Tools such as AlphaFold have revolutionized protein structure prediction. They are also enabling variant effect prediction and functional annotation of proteins, as well as opening up new possibilities for protein design. However, these technological advances must be balanced with sustainable computing practices.
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