Publications and manuscripts of the Machine Learning in Biomedicine group
Selected publications
- Sanjaya, Maljanen, Katainen, Waszak, Genomics England Research Consortium, Aaltonen, Stegle, Korbel, Pitkänen. Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping. Genome Medicine 15:47 (2023). doi:10.1186/s13073-023-01204-4. [preprint] [code]
- Timonen, Kerkelä, Impola, Penna, Partanen, Kilpivaara, Arvas, Pitkänen. DeepIFC: virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning. Cytometry Part A (2023). doi:10.1002/cyto.a.24770 [preprint] [code]
- Tagmazian, Schwarz, Lange, Pitkänen*, Vuoksimaa*. ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images. European Journal of Neuroscience (2024). doi:10.1111/ejn.16332 [preprint] [code]
- Pohjonen, Stürenberg, Rannikko*, Mirtti*, Pitkänen*, Spectral decoupling for training transferable neural networks in medical imaging. iScience 25, 2 (2022). doi: 10.1016/j.isci.2022.103767 [preprint]
- The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Pan-cancer analysis of whole genomes. Nature 578, 82-93 (2020). doi: 10.1038/s41586-020-1969-6.
- Katainen, Donner, Cajuso, Kaasinen, Palin, Mäkinen, Aaltonen, Pitkänen, Discovery of potential causative mutations in human coding and noncoding genome with the interactive software BasePlayer. Nature Protocols 13, 2580 (2018). doi: 10.1038/s41596-018-0052-3.
- Kondelin, Gylfe, Lundgren, Tanskanen, Hamberg, Aavikko, Palin, Ristolainen, Katainen, Kaasinen, Taipale, Taipale, Renkonen-Sinisalo, Järvinen, Böhm, Mecklin, Vahteristo, Tuupanen, Aaltonen, Pitkänen, Comprehensive evaluation of protein coding mononucleotide microsatellites in microsatellite-unstable colorectal cancer. Cancer Research 77, 4078–4088 (2017). doi: 10.1158/0008-5472.CAN-17-0682.
Manuscripts
- Pohjonen, Stürenberg, Föhr, Pitkänen*, Rannikko*, Mirtti*. Exposing and addressing the fragility of neural networks in digital pathology. arXiv (2022). doi:10.48550/arXiv.2206.15274. [code]
- Högel-Starck, Timonen, Atarsaikhan, Mogollon, Polso, Hassinen, Honkanen, Soini, Ruokoranta, Ahlnas, Juvila, Miettinen, Rodosthenous, Arvas, FinnGen, Heckman, Partanen, Daly, Palotie, Paavolainen*, Pietiäinen*, Pitkänen*. Morphological single-cell analysis of peripheral blood mononuclear cells from 390 healthy blood donors with Blood Cell Painting. bioRxiv (2024). doi:10.1101/2024.05.17.594648. [code]
- Koski, Langohr, Räisänen, Lahtinen, Hakkarainen, Heckman, Wartiovaara-Kautto*, Pitkänen*, Kilpivaara*. Pathogenicity evaluation of coding germline variants identifies rare alleles enriched in hematological patients of a founder population. medRxiv (2024). doi: 2024.10.23.24315723. [code]
Reviews
- Meriranta, Pitkänen, Leppä. Blood Has Never Been Thicker: Cell-free DNA Fragmentomics in the Liquid Biopsy Toolbox of B-cell Lymphomas. Seminars in Hematology (2023).
- Ji, Li, Sun, Dong, Taalas, Zhang, Wu, Pitkänen, Marttinen. A unified review of deep learning for automated medical coding. ACM Computing Surveys (2024).
All publications in Google Scholar and PubMed.