Nikita Moshkov

The paper introduces a method to link single-cell imaging and gene expression data through multimodal droplet barcoding, identifying genes correlated with phenotypic features.

The paper presents a comprehensive genetic perturbation dataset using Cell Painting to analyze gene expression effects in human cells, revealing novel gene clusters and functional relationships.

This study creates a high-resolution morpho-proteomic atlas of mitosis by decomposing it into 40 subsections and quantifying 4,350 proteins, revealing dynamic changes and links to cancer mutations.

The paper develops a weakly supervised learning method to learn treatment effect representations from imaging data, improving downstream analysis performance.

Self-supervised DINO features, learned without labels, describe cell morphology from subcellular detail to experimental groups and expose a hierarchy of biological and technical variation in three public datasets.

The paper predicts compound activity using chemical structures, images, and gene-expression profiles, showing that combining these modalities can improve prediction accuracy in drug discovery.

PyLAE predicts local ancestry from whole-genome sequencing or high-density genotyping data, efficiently handling large numbers of ancestral populations.

AnnotatorJ is an ImageJ plugin that combines deep learning and manual annotation to improve cell segmentation and annotation efficiency.

This paper applies test-time augmentation to improve cell segmentation accuracy in microscopy images using U-Net and Mask R-CNN models, achieving a better score in the DSB 2018 competition.