Christopher A. Jackson

This study identifies spatially resolved aging and senescence signatures in the human hippocampus using multi-omics approaches, revealing cell-type and sex-specific gene expression changes.

The paper maps cognitive impairments in ALS by integrating spatial and single-nucleus transcriptomics, revealing distinct cellular and molecular patterns in language and executive dysfunction.

This study uses multi-omics data to compare molecular signatures in the motor cortex and lumbar spinal cord of ALS patients, finding distinct alterations in MN and glial functions depending on symptom onset site.

This study identifies TDP-43-dependent cryptic gene-TE fusions (crypTEs) in neurons and ALS/FTD tissues, showing their association with altered gene expression and potential pathogenic effects.

This paper introduces a deep learning framework for gene regulatory network inference and latent transcription factor activity estimation, showing improved performance over existing methods.

This paper develops a deep learning model to estimate gene regulatory networks and RNA kinetics from single-cell RNA-seq data, showing improved recovery of known regulatory relationships and predictive power.

The paper introduces Inferelator 3.0, a tool for inferring gene regulatory networks from single-cell data, showing improved performance on yeast and mouse brain datasets.

This paper introduces DEWAKSS, a method for denoising single-cell genomics data by optimally tuning kNN- and diffusion-based approaches to preserve biological variance and improve clustering.

The paper develops a method to reconstruct gene regulatory networks from scRNA-seq data by barcoding gene deletion mutants in budding yeast across diverse environments.