Alicja Tadych

The paper builds an InterVir Atlas to map temporal interactome changes across RNA viruses and identifies convergent host vulnerabilities for antiviral drug repurposing.

SPEEDI automates single-cell multi-omics analysis by integrating data, inferring batches, and labeling cell types without user input, improving reproducibility and accessibility.

The paper identifies region-specific astrocyte genes and pathways linked to Alzheimer's disease susceptibility using a network-guided approach and mouse gene expression data.

DeepArk uses deep learning to model cis-regulatory codes in four model species, accurately predicting regulatory features and variant impacts.

This study identifies a functional gene module associated with selective neuronal vulnerability in Alzheimer’s disease using integrated molecular and genetic data from mice and humans.

This study analyzes the transcriptomes of four major tissues in adult C. elegans to identify tissue-specific genes, isoforms, and regulatory patterns, providing a resource for understanding adult multicellular biology.

This study uses a machine-learning approach to predict autism-associated genes and identifies key pathways and developmental stages involved in ASD.