J. Matthew Mahoney

The paper uses bag-of-waves classifiers to predict epilepsy genotypes in mice from EEG data, achieving high accuracy and specificity.

This paper identifies novel epilepsy modifier genes in mice through meta-analysis of GWAS data and functional network analysis, highlighting genes involved in neurodevelopmental processes not targeted by current therapies.

This study uses supervised machine learning to automatically quantify seizure severity from video data in mice, enabling high-throughput and standardized seizure scoring.

The paper identifies a composite transcriptome signature with high distal heritability that explains 30% of metabolic trait variation across species.

This study shows that developmental insults impair mPFC network dynamics, and ACTH treatment prevents these impairments, preserving cognitive function through restored network organization.

This study shows ACTH prevents anxiety-like behaviors and normalizes astrocyte proteins after early life seizures via MC4R activation in neurons and astrocytes.

The paper identifies eQTL in mouse neural progenitor cells and shows their effects on gene regulation across developmental stages.

The paper introduces TAVAC, a metric to quantify overfitting and interpretation reproducibility in Vision Transformer models using attention consistency across training and validation data.

This study examines how Fmr1 knockout rats exhibit behavioral and neural network impairments linked to autism-like symptoms, suggesting that restoring neural dynamics may improve outcomes.

This study uses genetically diverse mice to identify genetic loci associated with different disease progression patterns in multiple sclerosis.

SPiRiT uses explainable deep learning to predict spatial single-cell gene expression from histological images, improving accuracy over existing methods.

This study identifies new genetic loci and candidate genes associated with tuberculosis susceptibility in mice using systems genetics approaches.

The paper identifies driver genes for type 2 diabetes using causal network perturbation analysis and single-cell RNA sequencing in mouse models.

The paper examines how histone modifications vary across mouse strains and correlates these variations with gene expression, finding strong links between chromatin states and gene expression, but not DNA methylation.

This paper introduces a multi-network gene prioritization method for Alzheimer's Disease that complements mQTL-based approaches, showing significant concordance in prioritizing risk genes.

The paper investigates how ACTH and dexamethasone affect gene expression pathways in rats after early-life seizures, showing ACTH normalizes expression while Dex does not.

This study examines how kinship correction affects genetic interaction test statistics in mouse populations, finding no inflation in interaction tests but recommending reduced kinship matrices for better correction.

This study identifies a three-gene network influencing lung function and autoantibody status in systemic sclerosis patients through genetic interaction analysis.

The paper uses network-based functional prediction to prioritize candidate genes in a mouse histamine hypersensitivity QTL, identifying genes with strong functional associations and SNP proximity.

The paper investigates how spatial working memory is encoded and maintained in the hippocampal-prefrontal network using single-unit recordings in rats, finding altered dynamics in a disease model.

The paper shows environmental enrichment improves cognitive function in rats with hippocampal malformations by restoring abnormal spike timing and rate coding in hippocampal neurons.

The paper performs a meta-analysis of gene expression data from SSc clinical trials to identify therapeutic targets and predict treatment response using machine learning.

The paper identifies a common immune-fibrotic gene expression signature across SSc, PF, and PAH and uses it to uncover tissue-specific cellular modulators of fibrosis.