Artem Sokolov

Temporal Biodynamics: An AI Platform for Identification of Stage-Relevant Targets and Biomarkers

Temporal Biodynamics identifies stage-relevant targets and biomarkers by modeling disease progression using AI, improving detection of disease-associated genes and proteins over traditional methods.

Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action

This study introduces DECIPHAER, a tool that identifies critical mechanisms of tuberculosis drug action by integrating multi-modal data, revealing that respiration inhibition is more impactful than cell wall effects.

Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational Autoencoders

This paper introduces a method that combines autoencoders with segmentation to improve single-cell analysis of multiplexed tissue images by capturing morphology and reducing signal spillover.

TYK2 as a novel therapeutic target in Alzheimer Disease

The paper identifies TYK2 as a potential therapeutic target for neurodegenerative diseases by linking cdsRNA-induced inflammation to interferon signaling and demonstrating protective effects of TYK2 inhibitors.

Addressing persistent challenges in digital image analysis of cancerous tissues

The paper discusses challenges in digital image analysis of cancerous tissues and outlines solutions developed during a 2022 hackathon.

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

The paper introduces MAMS, a metadata standard for single-cell data matrices and analysis workflows, to improve reproducibility and interoperability across datasets.

Paired evaluation defines performance landscapes for machine learning models

This study introduces paired evaluation to improve performance estimation of machine learning models in drug response and disease severity prediction, showing up to 30% variation in performance estimates based on test data choice.

A Web-based Software Resource for Interactive Analysis of Multiplex Tissue Imaging Datasets

Galaxy-ME is a web-based software hub for analyzing and visualizing multiplex tissue imaging datasets, demonstrating its utility on normal and cancerous tissue data.

MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging

MCMICRO processes multiplexed tissue images into single-cell data, demonstrating its versatility across different tissues and imaging platforms.

A community-based approach to image analysis of cells, tissues and tumors

The paper discusses computational challenges in analyzing multiplexed imaging data and outlines community-driven approaches to address them through workshops and hackathons.

Machine Learning Identifies Novel Candidates for DrugRepurposing in Alzheimer's Disease

The paper presents a machine learning framework to identify FDA-approved drugs for repurposing in Alzheimer's Disease by analyzing gene lists associated with disease severity and molecular mechanisms.

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Facetto combines unsupervised and supervised learning to analyze multi-channel microscopy images, enabling discovery of cell types and phenotypes in cancer tissues.