Research output per year
Research output per year
Personal profile
Short presentation
I am a professor in the Department of Computer Science and the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. I have a Ph.D. in theoretical physics, but moved into machine learning and bioinformatics during my PhD. I have worked at UCSC, the Sanger Centre in Cambridge and at the Technical University of Denmark before joining the University of Copenhagen in 2002, where I was affiliated with the Department of Biology until 2020. I have worked in many areas of bioinformatics and machine learning, both with theory and applications. I am probably most well-known for my early work on hidden Markov models for biological sequences. In recent years I have focussed on deep generative models and applied them to gene expression data and other bio/medical data.
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Research output
-
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 AccessFile1 Citation (Scopus)31 Downloads (Pure) -
Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology
D'Amico, S., Dall'Olio, D., Sala, C., Dall'Olio, L., Sauta, E., Zampini, M., Asti, G., Lanino, L., Maggioni, G., Campagna, A., Ubezio, M., Russo, A., Bicchieri, M. E., Riva, E., Tentori, C. A., Travaglino, E., Morandini, P., Savevski, V., Santoro, A., Prada-Luengo, I. & 9 others, , 2023, In: JCO clinical cancer informatics. 7, p. 1-22 e2300021.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile47 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 AccessFile2 Citations (Scopus)23 Downloads (Pure) -
Context dependency of nucleotide probabilities and variants in human DNA
Liang, Y., Grønbæk, C., Fariselli, P. & Krogh, A., 2022, In: BMC Genomics. 23, 1, 15 p., 87.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile3 Citations (Scopus)61 Downloads (Pure) -
Context dependent prediction in DNA sequence using neural networks
Grønbæk, C., Liang, Y., Elliott, D. & Krogh, A., 2022, In: PeerJ. 10, 22 p., e13666.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile38 Downloads (Pure) -
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 AccessFile2 Citations (Scopus)47 Downloads (Pure) -
High-throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype-specific serologically relevant biomarkers
Terkelsen, T., Pernemalm, M., Gromov, P., Borresen-Dale, A-L., Krogh, A., Haakensen, V. D., Lethio, J., Papaleo, E. & Gromova, I., 2021, In: Molecular Oncology. 15, 2, p. 429-461 33 p.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile21 Citations (Scopus)53 Downloads (Pure) -
CAncer bioMarker Prediction Pipeline (CAMPP) - A standardized framework for the analysis of quantitative biological data
Terkelsen, T., Krogh, A. & Papaleo, E., 2020, In: PLOS Computational Biology. 16, 3, 20 p., e1007665.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile12 Citations (Scopus)126 Downloads (Pure)