Viktoria Schuster

Search results

• 2024

  multiDGD: A versatile deep generative model for multi-omics data

  Schuster, V., Dann, E., Krogh, A. & Teichmann, S. A., 2024, In: Nature Communications. 15, 1, 16 p., 10031.

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

  Open Access

  File
  2 Citations (Scopus)

  3 Downloads (Pure)
• 2023

  N-of-one differential gene expression without control samples using a deep generative model

  Prada-Luengo, I., Schuster, V., Liang, Y., Terkelsen, T., Sora, V. & Krogh, A., 2023, In: Genome Biology. 24, 1, 17 p., 263.

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

  Open Access

  File
  1 Citation (Scopus)

  40 Downloads (Pure)

• The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data

  Schuster, V. & Krogh, A., 2023, In: Bioinformatics. 39, 9, 14 p., btad497.

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

  Open Access

  File
  3 Citations (Scopus)

  29 Downloads (Pure)
• 2021

  A manifold learning perspective on representation learning: Learning decoder and representations without an encoder

  Schuster, V. & Krogh, A., Nov 2021, In: Entropy. 23, 11, 14 p., 1403.

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

  Open Access

  File
  3 Citations (Scopus)

  55 Downloads (Pure)

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

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

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

  Open Access

  File
  110 Citations (Scopus)

  59 Downloads (Pure)