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  • Machine Learning for Protein Science and Engineering by Christian Dallago book cover
  • Machine Learning for Protein Science and Engineering by Christian Dallago book cover
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Machine Learning for Protein Science and Engineering

by Christian Dallago

Machine learning across protein research

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AlbakiReads Editorial

Machine learning across protein research

This expert-contributed collection maps several points of contact between machine learning and protein science. Its coverage ranges from protein representation learning, statistical co-evolution models, and large-scale homology searches to applications in structure prediction, functional annotation, and variant-effect prediction. Generative models for protein sequences and structures bring the discussion to protein design, while a section on environmental impact considers the computational costs alongside the research possibilities. Together, these topics make the book a field-spanning reference on methods and applications, with attention to both scientific uses and the sustainability questions raised by large-scale computation.

Good fit for readers who enjoy

  • Scientists studying proteins or computational biology who want an overview of machine-learning methods used in the field.
  • Researchers interested in protein structure prediction, annotation, variant effects, or design.
  • Institutional research teams considering machine-learning tools and their computational footprint.

Themes

Machine learning in protein scienceProtein structure prediction and designFunctional annotation and variant-effect predictionSustainable computing

Based on publisher information and book metadata.

Book overview

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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