Tamás Balassa

Spapros selects optimal probe sets for targeted spatial transcriptomics to improve cell type identification and spatial variation detection across multiple datasets.

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

The paper develops an automated patch clamp system using deep learning and image analysis to measure neurons in human and rodent brain slices.

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.

This paper develops a deep convolutional neural network for automated astrocyte detection in microscopy images, achieving accuracy comparable to human experts and faster runtime than existing methods.

The paper shows that incorporating microenvironmental features improves machine learning-based single-cell phenotyping in imaging data.