@article{discovery10199741,
         journal = {Nucleic Acids Research},
           title = {Deep learning for the PSIPRED Protein Analysis Workbench},
           pages = {W287--W293},
            note = {{\copyright} The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.},
          volume = {52},
       publisher = {OXFORD UNIV PRESS},
          number = {W1},
            year = {2024},
           month = {July},
            issn = {0305-1048},
        abstract = {The PSIRED Workbench is a long established and popular bioinformatics web service offering a wide range of machine learning based analyses for characterizing protein structure and function. In this paper we provide an update of the recent additions and developments to the webserver, with a focus on new Deep Learning based methods. We briefly discuss some trends in server usage since the publication of AlphaFold2 and we give an overview of some upcoming developments for the service. The PSIPRED Workbench is available at http://bioinf.cs.ucl.ac.uk/psipred.},
          author = {Buchan, Daniel WA and Moffat, Lewis and Lau, Andy and Kandathil, Shaun M and Jones, David T},
             url = {http://dx.doi.org/10.1093/nar/gkae328},
        keywords = {Science \& Technology, Life Sciences \& Biomedicine, Biochemistry \& Molecular Biology, PREDICTION, ACCURACY}
}