Lassi Paavolainen

TERRA is a foundation model for spatial transcriptomics that predicts tissue behavior under perturbation and identifies multi-scale biological insights across tissues.

This study shows SARS-CoV-2 infects hiPSC-derived neurons via endosomal-lysosomal pathway dependent on cathepsins, leading to HIF1alpha accumulation and phosphorylated tau, suggesting neurodegenerative effects.

The paper uses machine learning to analyze chromatin dynamics during serum starvation, revealing chromosome-specific responses and differential behavior between homologous chromosomes.

The paper develops a self-supervised deep learning framework to analyze multiplexed cancer tissue microscopy images, stratifying patients into prognostically distinct groups with different survival outcomes.

The paper identifies FAP-expressing EMT-associated cancer cells in highly inflamed localized ccRCC as an aggressive, immunosuppressive subtype linked to poor prognosis and metastasis.

MorphoNavigator-3D enables automated, annotation-free single-cell analysis of 3D tumor-immune co-cultures, revealing drug-induced phenotypes and their biological relevance.

Piximi provides a no-programming web tool for biological image analysis using deep learning, enabling rapid insights without installation or coding.

The paper develops Blood Cell Painting (BCP) to profile immune cell morphology in healthy donors, identifying genetic and clinical associations with immune cell features.

The paper introduces Regression Plane, a tool for continuous phenotypic analysis using machine learning without requiring class labels.

BIAFLOWS provides a framework to document, deploy, and benchmark bioimage analysis workflows, enabling reproducible and fair comparisons of image analysis methods.

The paper introduces a deep learning framework for nucleus segmentation that uses image style transfer to adapt to new data without expert annotations, outperforming existing methods on diverse microscopy images.