Protein Function Prediction
Methods and Protocols
Herausgegeben: Kurgan, Lukasz; Kihara, Daisuke
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This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational techniques, such as machine learning, multi-task learning, protein language models, and deep learning, the book continues by covering specific tools for protein function prediction, ranging from gene ontology-term predictions to the predictions of binding sites, protein localization and solubility, signal peptides, intrinsic disorder, and intrinsically disordered binding regions, as well as prese...
This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational techniques, such as machine learning, multi-task learning, protein language models, and deep learning, the book continues by covering specific tools for protein function prediction, ranging from gene ontology-term predictions to the predictions of binding sites, protein localization and solubility, signal peptides, intrinsic disorder, and intrinsically disordered binding regions, as well as presenting databases that address protein moonlighting and protein binding. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, step-by-step instructions on how to use software and web resources, use cases, and tips on troubleshooting and avoiding known pitfalls.
Authoritative and up-to-date, Protein Function Prediction: Methods and Protocols, Second Edition helps readers to understand and appreciate this vibrant and growing research area and guides in the quest to identify and use the best computational methods and resources for their projects.
Authoritative and up-to-date, Protein Function Prediction: Methods and Protocols, Second Edition helps readers to understand and appreciate this vibrant and growing research area and guides in the quest to identify and use the best computational methods and resources for their projects.
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