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Maria Littmann

Showing results (1-10 of 16) with videos related to

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BMC Bioinformatics|July 20, 2019
FunFam protein families improve residue level molecular function predictionLinus Scheibenreif, Maria Littmann, Christine Orengo, et al.
Scientific Reports|December 14, 2021
Protein embeddings and deep learning predict binding residues for various ligand classesMaria Littmann, Michael Heinzinger, Christian Dallago, et al.
BMC Bioinformatics|April 25, 2019
Detailed prediction of protein sub-nuclear localizationMaria Littmann, Tatyana Goldberg, Sebastian Seitz, et al.
Scientific Reports|January 14, 2021
Embeddings from deep learning transfer GO annotations beyond homologyMaria Littmann, Michael Heinzinger, Christian Dallago, et al.
BMC Bioinformatics|December 22, 2019
Correction to: Detailed prediction of protein sub-nuclear localizationMaria Littmann, Tatyana Goldberg, Sebastian Seitz, et al.
BMC Research Notes|October 3, 2025
Toxin data quality: a critical examination of bacterial exotoxins and animal toxinsTanja Krüger, Ivan Koludarov, Maria Littmann, et al.
NAR Genomics and Bioinformatics|June 15, 2022
Contrastive learning on protein embeddings enlightens midnight zoneMichael Heinzinger, Maria Littmann, Ian Sillitoe, et al.
Bioinformatics (Oxford, England)|May 12, 2021
Clustering FunFams using sequence embeddings improves EC purityMaria Littmann, Nicola Bordin, Michael Heinzinger, et al.
Trends in Biochemical Sciences|December 12, 2022
Novel machine learning approaches revolutionize protein knowledgeNicola Bordin, Christian Dallago, Michael Heinzinger, et al.
Bioinformatics (Oxford, England)|January 17, 2023
CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language modelsVamsi Nallapareddy, Nicola Bordin, Ian Sillitoe, et al.
Pageof 2

Showing results (1-10 of 16) with videos related to

Sort By:
Pageof 2
BMC Bioinformatics|July 20, 2019
FunFam protein families improve residue level molecular function predictionLinus Scheibenreif, Maria Littmann, Christine Orengo, et al.
Scientific Reports|December 14, 2021
Protein embeddings and deep learning predict binding residues for various ligand classesMaria Littmann, Michael Heinzinger, Christian Dallago, et al.
BMC Bioinformatics|April 25, 2019
Detailed prediction of protein sub-nuclear localizationMaria Littmann, Tatyana Goldberg, Sebastian Seitz, et al.
Scientific Reports|January 14, 2021
Embeddings from deep learning transfer GO annotations beyond homologyMaria Littmann, Michael Heinzinger, Christian Dallago, et al.
BMC Bioinformatics|December 22, 2019
Correction to: Detailed prediction of protein sub-nuclear localizationMaria Littmann, Tatyana Goldberg, Sebastian Seitz, et al.
BMC Research Notes|October 3, 2025
Toxin data quality: a critical examination of bacterial exotoxins and animal toxinsTanja Krüger, Ivan Koludarov, Maria Littmann, et al.
NAR Genomics and Bioinformatics|June 15, 2022
Contrastive learning on protein embeddings enlightens midnight zoneMichael Heinzinger, Maria Littmann, Ian Sillitoe, et al.
Bioinformatics (Oxford, England)|May 12, 2021
Clustering FunFams using sequence embeddings improves EC purityMaria Littmann, Nicola Bordin, Michael Heinzinger, et al.
Trends in Biochemical Sciences|December 12, 2022
Novel machine learning approaches revolutionize protein knowledgeNicola Bordin, Christian Dallago, Michael Heinzinger, et al.
Bioinformatics (Oxford, England)|January 17, 2023
CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language modelsVamsi Nallapareddy, Nicola Bordin, Ian Sillitoe, et al.
Pageof 2