Ole Winther

Ole Winther

Professor with special responsibilities, Professor MSO

Member of:

    ORCID: 0000-0002-1966-3205

    Publication year:
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    1 - 5 out of 5Page size: 10
    1. 2019
    2. Published

      SignalP 5.0 improves signal peptide predictions using deep neural networks

      Armenteros, J. J. A., Tsirigos, Konstantinos, Sønderby, C. K., Petersen, T. N., Winther, Ole, Brunak, Søren, von Heijne, G. & Nielsen, H., 18 Feb 2019, In: Nature Biotechnology. 37, 4, p. 420-423

      Research output: Contribution to journal › Journal article › Communication

    3. Published

      A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning

      Bhowmik, A., Castelli, I. E., Garcia-Lastra, J. M., Bjørn-Jørgensen, P., Winther, Ole & Vegge, T., 2019, In: Energy Storage Materials. 21, p. 446-456

      Research output: Contribution to journal › Review › Research › peer-review

    4. Published

      Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data

      Kinalis, S., Nielsen, Finn Cilius, Winther, Ole & Bagger, F. O., 2019, In: BMC Bioinformatics. 20, 9 p., 379.

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

    5. Published

      Detecting sequence signals in targeting peptides using deep learning

      Almagro Armenteros, J. J., Salvatore, Marco, Emanuelsson, O., Winther, Ole, von Heijne, G., Elofsson, A. & Nielsen, H., 2019, In: Life Science Alliance. 2, 5, 14 p., 201900429.

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

    6. Published

      NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning

      Klausen, M. S., Jespersen, M. C., Nielsen, H., Jensen, K. K., Jurtz, V. I., Sønderby, C. K., Sommer, M. O. A., Winther, Ole, Nielsen, M., Petersen, Bent & Marcatili, P., 2019, In: Proteins: Structure, Function and Bioinformatics. 87, 6, p. 520-527

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

    ID: 171145930