Ole Winther

Ole Winther

Professor with special responsibilities, Visiting professor


  1. 2022
  2. Published

    DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

    Thumuluri, V., Almagro Armenteros, J. J., Johansen, A. R., Nielsen, H. & Winther, Ole, 2022, In: Nucleic Acids Research. 50, W1, p. W228-W234

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Published

    Interpretable autoencoders trained on single cell sequencing data can transfer directly to data from unseen tissues

    Walbech, J. S., Kinalis, S., Winther, Ole, Nielsen, Finn Cilius & Bagger, F. O., 2022, In: Cells. 11, 12 p., 85.

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Published

    NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning

    Høie, M. H., Kiehl, E. N., Petersen, Bent, Nielsen, M., Winther, Ole, Nielsen, H., Hallgren, J. & Marcatili, P., 2022, In: Nucleic Acids Research. 50, W1, p. W510-W515 6 p.

    Research output: Contribution to journalJournal articleResearchpeer-review

  5. Published

    SignalP 6.0 predicts all five types of signal peptides using protein language models

    Teufel, Felix Georg, Almagro Armenteros, J. J., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, Konstantinos, Winther, Ole, Brunak, Søren, von Heijne, G. & Nielsen, H., 2022, In: Nature Biotechnology. 40, p. 1023-1025

    Research output: Contribution to journalComment/debateResearch

  6. Published

    Transfer learning reveals sequence determinants of regulatory element accessibility

    Salvatore, Marco, Horlacher, M., Winther, Ole & Andersson, Robin, 2022, bioRxiv, 24 p.

    Research output: Working paperPreprintResearch

  7. Published

    Transition1x: a dataset for building generalizable reactive machine learning potentials

    Schreiner, M., Bhowmik, A., Vegge, T., Busk, J. & Winther, Ole, 2022, In: Scientific Data. 9, 1, 9 p., 779.

    Research output: Contribution to journalJournal articleResearchpeer-review

  8. 2021
  9. Published

    A comparison of tools for copy-number variation detection in germline whole exome and whole genome sequencing data

    Gabrielaite, M., Torp, M., Rasmussen, M., Andreu-Sánchez, S., Vieira, F., Pedersen, C., Kinalis, S., Madsen, M., Yde, C., Rønn, O. L., Marvig, R., Østrup, O., Rossing, Caroline Maria, Nielsen, Finn Cilius, Winther, Ole & Bagger, F. O., 2021, bioRxiv, 29 p.

    Research output: Working paperPreprintResearch

  10. Published

    A comparison of tools for copy-number variation detection in germline whole exome and whole genome sequencing data

    Gabrielaite, M., Torp, M. H., Rasmussen, Malthe Sebro, Andreu-Sánchez, S., Vieira, F. G., Pedersen, C. B., Kinalis, S., Madsen, M. B., Kodama, M., Demircan, G. S., Simonyan, A., Yde, C. W., Olsen, L. R., Marvig, R. L., Østrup, O., Rossing, Caroline Maria, Nielsen, Finn Cilius, Winther, Ole & Bagger, F. O., 2021, In: Cancers. 13, 24, 6283.

    Research output: Contribution to journalJournal articleResearchpeer-review

  11. Published

    Improved metagenome binning and assembly using deep variational autoencoders

    Nissen, J. N., Johansen, Joachim, Allesoe, R. L., Sonderby, C. K., Armenteros, J. J. A., Grønbech, Christopher Heje, Jensen, Lars Juhl, Nielsen, H. B., Petersen, T. N., Winther, Ole & Rasmussen, Simon, 2021, In: Nature Biotechnology. 39, p. 555-560

    Research output: Contribution to journalJournal articleResearchpeer-review

  12. Published

    NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data

    Montemurro, A., Schuster, Viktoria, Povlsen, H. R., Bentzen, A. K., Jurtz, V., Chronister, W. D., Crinklaw, A., Hadrup, S. R., Winther, Ole, Peters, B., Jessen, L. E. & Nielsen, M., 2021, In: Communications Biology . 4, 13 p., 1060.

    Research output: Contribution to journalJournal articleResearchpeer-review

ID: 171145930