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Machine Learning Tools to Study Non-Globular proteins

Year: 2024

Type: article

Abstract: ML4NGP COST Action CA21160 - Deliverable 5 “Report on state of the art ML approaches focused on different phenomena of NGPs”

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14200160

Authors: Poley-Gil, Miriam, Ćirić, Nevena, Fernandez-Martin, Miguel, Garcia-Pardo, Javier, Petrosyan, Rafayel, Gruca, Aleksandra, Grynberg, Marcin, Monzon, Alexander M., Kovačević, Jovana, Parra, R. Gonzalo

Institutions: Barcelona Supercomputing Center, University of Belgrade, Universitat Autònoma de Barcelona, American University of Armenia, L. A. Orbeli Institute of Physiology NAS RA, Silesian University of Technology, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, University of Padua, Istraživačko-razvojni institut za veštačku inteligenciju Srbije

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: European Cooperation in Science and Technology

Award: G7954268596

OpenAlex ID: W6930603835

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