Ron M. Stewart

Microenvironmental and transcriptional determinants of the robustness of spinal cord regeneration in zebrafish

The paper identifies hoxb5a as a critical regulator of spinal cord regeneration in zebrafish by analyzing transcriptional and microenvironmental differences between successful and failed regeneration cases.

Quantifying Evidence for Competing Biomedical Hypotheses using Large Language Models and Bayesian Analysis

This paper introduces KM-GPT-DCH, a method using LLMs and Bayesian analysis to evaluate and compare biomedical hypotheses by analyzing scientific literature.

SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation

SKiM-GPT evaluates biomedical hypotheses by combining literature-based discovery with large language models to assess hypothesis validity using retrieved PubMed abstracts.

Diminished Immune Cell Adhesion in Hypoimmune ICAM-1 Knockout Pluripotent Stem Cells

This paper identifies ICAM-1 as a novel hypoimmune target in human pluripotent stem cells, showing that its knock-out reduces immune cell adhesion and enhances graft survival.

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

SKiM discovers and annotates biomedical knowledge using co-occurrence and transformer models to find A-B-C relationships between user-defined concepts.

A Nile Grass Rat Transcriptomic Landscape Across 22 Organs By Ultra-deep Sequencing and Comparative RNA-seq pipeline (CRSP)

The paper develops CRSP, a pipeline for RNA-seq analysis in species without a sequenced genome or reference transcripts, and applies it to generate a comprehensive Nile grass rat transcriptomic atlas.

A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes

This paper presents a haplotype-resolved genome assembly of the Nile rat, revealing unique genomic features relevant to type 2 diabetes and metabolic dysfunctions.

SpatialCorr: Identifying Gene Sets with Spatially Varying Correlation Structure

SpatialCorr identifies gene sets with spatially varying correlation structure in spatial transcriptomics data, revealing insights not found with existing methods.

Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data

This paper introduces SINGE, a method for gene regulatory network inference from ordered single-cell transcriptomic data, showing improved performance over existing methods in mouse stem cell differentiation studies.

Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization

This study shows that equalizing cDNA library concentrations before pooling improves gene detection rates and reduces technical artifacts in single-cell RNA-seq data.

Interspecies Chimeric Conditions Affect the Developmental Rate of Human Pluripotent Stem Cells

This study shows that co-differentiating human and mouse pluripotent stem cells accelerates human neural differentiation, with gene expression changes correlating with in utero development.

SKiM - A generalized literature-based discovery system for uncovering novel biomedical knowledge from PubMed

SKiM is a literature-based discovery system that rediscovered biomedical knowledge and identified potential drug candidates by analyzing PubMed literature.

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis

This study evaluates the reproducibility of spatial ordering analysis across different single-cell RNA-seq protocols, finding high reproducibility despite differences in sequencing depth and computational methods.

Dynamic regulatory module networks for inference of cell type-specific transcriptional networks

This paper introduces DRMN, a method to infer cell type-specific transcriptional networks from multi-omic data, showing it accurately predicts cis-regulators and models network dynamics.

Trendy: Segmented regression analysis of expression dynamics for high-throughput ordered profiling experiments

Trendy uses segmented regression to analyze gene expression dynamics in ordered experiments, identifying gene-specific patterns and overall transcriptome changes.

SCnorm: A quantile-regression based approach for robust normalization of single-cell RNA-seq data

SCnorm provides a quantile-regression based normalization method for scRNA-seq data to reduce artifacts and improve downstream analysis accuracy.