Machine learning
techniques are having a huge impact on how biologists study and
understand proteins. Protein structure prediction has been
revolutionized, and new tools are improving functional annotation of
proteins, as well as opening up new possibilities for protein design.
Written and edited by experts in the field, this collection from Cold Spring Harbor Perspectives in Biology
explores the rapidly evolving intersection of machine learning and
protein science. The contributors review various approaches for learning
representations of proteins, as well as statistical models of
co-evolution and large-scale homology searches, which have important
implications for protein structure prediction. In addition, they examine
applications of machine learning for functional annotation of proteins
and variant effect prediction.
The collection also explores
generative models for protein sequence and structure and looks at the
environmental impact of applying these tools, acknowledging the need to
balance technological advancement with sustainable computing. It is
therefore an essential reference for all scientists interested in both
learning more about these techniques and implementing them in research
institutions.
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