Ole Winther

Ole Winther

Professor with special responsibilities, Visiting professor, Professor MSO

Member of:

    ORCID: 0000-0002-1966-3205

    Publication year:
    All
    1 - 6 out of 6Page size: 10
    1. 2022
    2. Published

      Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks

      Busk, J., Jørgensen, P. B., Bhowmik, A., Schmidt, M. N., Winther, Ole & Vegge, T., 2022, In: Machine Learning: Science and Technology. 3, 1, 12 p., 015012.

      Research output: Contribution to journal › Journal article › Research › peer-review

    3. 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 journal › Journal article › Research › peer-review

    4. 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 journal › Journal article › Research › peer-review

    5. 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 journal › Journal article › Research › peer-review

    6. 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 journal › Comment/debate › Research

    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 journal › Journal article › Research › peer-review

    ID: 171145930