Deep learning models of the causes and consequences of somatic mutations in cancer.
Sanjaya, Pitkänen
bioRxiv
The MuAt models packaged for Docker and Bioconda, reproducing published accuracy and reaching 81% in the Genomics England secure environment without retraining.
Sanjaya, Pitkänen
medRxiv
MuAt2 classifies tumour type and subtype jointly across 14,527 Genomics England whole genomes, with embeddings that stratify glioma prognosis and infer origins of unknown primaries.
Y. Koski, Patel, Kakko von Koch, Jouhten, Aaltonen, Palin#, Sahu#, Pitkänen#
bioRxiv
A statistical toolkit for nanopore signal that separates four adduct-inducing compounds by their effects on base quality, ionic current and translocation dynamics.
Determinants of blood cell phenotype variation, from single-cell imaging and genotyping data.
Högel-Starck*, Timonen*, Atarsaikhan, Mogollon, Polso, Hassinen, Honkanen, Soini, Ruokoranta, Ahlnas, Juvila, Miettinen, Rodosthenous, Arvas, FinnGen, Heckman, Partanen, Daly, Palotie, Paavolainen#, Pietiäinen#, Pitkänen#
bioRxiv
Fluorescence imaging of 50 million mononuclear cells from 390 blood donors yields 18 morphology clusters and 93 genetic associations across 30 loci.
Timonen, Kerkelä, Impola, Penna, Partanen, Kilpivaara, Arvas, Pitkänen
Cytometry Part A
An Inception U-Net generates fluorescent marker images from brightfield imaging flow cytometry, typing blood cells without staining, including types unseen during training.
Germline determinants of hematological malignancies, modeled from high-throughput multiomics data.
Langohr*, Kaaja*, Douglas, Nebelung, J. Koski, Ikonen, Katainen, Maljanen, Hakkarainen, Räisänen, Niinimäki, Kakko, Siitonen, Adhikari, Vähä-Koskela, Heckman, Lahtela, Wartiovaara-Kautto#, Pitkänen#, Kilpivaara#
HemaSphere
Single-cell transcriptomics of patient marrow finds ferroptotic erythroid stress arising before TP53 mutation in both ERCC6L2 disease and Shwachman-Diamond syndrome.
J. Koski, Langohr, Räisänen, Lahtinen, Hakkarainen, Heckman, Wartiovaara-Kautto#, Pitkänen#, Kilpivaara#
medRxiv
An ACMG-guided pipeline over 511 Finnish hematological malignancy patients finds CHEK2 variants enriched 1.7-fold and candidate alleles in CUX2, RNPC3 and MFSD2A.
Meriranta, Pitkänen, Leppä
Seminars in Hematology
A review of cell-free DNA fragmentomics in B-cell lymphoma liquid biopsy: how fragment patterns form, the technical limits, and where machine learning fits.
Deep learning models that read pathology slides and brain scans, and how they behave under domain shift.
Pohjonen*, Batouche*, Kantola, Rannikko, Sandeman, Erickson, Pitkänen#, Mirtti#
Journal of Pathology Informatics 100715
A foundation model pre-trained on 48 million prostate tissue tiles outperforms natural-image pre-training and improves prostate cancer survival models when combined with clinical nomograms.
Tagmazian#, Schwarz, Lange, Pitkänen#, Vuoksimaa#
bioRxiv
A variational autoencoder over 3,110 amyloid PET scans separates four subgroups along the Alzheimer's continuum without preset positivity thresholds, two of them progressing faster.
Tagmazian, Schwarz, Lange, Pitkänen#, Vuoksimaa#
European Journal of Neuroscience
A residual network predicts cerebrospinal fluid amyloid-beta directly from amyloid PET scans (r = 0.92), without preselected reference regions or tracer-specific thresholds.
* equal contribution · # shared senior authorship