Jason H. Moore

An evidence-governed agent harness for spatial transcriptomics prioritizes ICAM-1 as a target in lung fibrosis

DeepMAST, an AI system for spatial transcriptomics, identifies ICAM-1 as a therapeutic target in lung fibrosis through evidence-based analysis and validation.

CCT2 expression marks a translation-activated state in sporadic ALS motor neurons and is associated with functional decline

The paper identifies CCT2 as a marker of a translation-activated state in sporadic ALS motor neurons and links it to functional decline.

ASAREE: An Analytical Sandbox for Agentic AI Research, Engineering, and Experimentation

ASAREE evaluates agentic AI system performance and resource efficiency through factorial experiments, showing that advanced models and critic agents increase costs without improving predictive performance.

EcoXAI: Autonomous Agentic Ecosystem for Explainable Artificial Intelligence and Biomedical Discovery

EcoXAI introduces a multi-agent system for biomedical discovery that integrates knowledge graphs and agentic AI to identify novel drug candidates for Alzheimer's Disease.

Novel Epistatic Interaction Between RBMS3 and CDKN2B-AS1 in Coronary Artery Disease Risk Identified by Machine Learning Tool VariantSpark

The paper identifies an epistatic interaction between RBMS3 and CDKN2B-AS1 in CAD risk using VariantSpark and BitEpi, with validation in UK Biobank and All of Us.

Beyond P-values: A Multi-Metric Framework for Robust Feature Selection and Predictive Modeling

The paper introduces MIXER, a framework that combines multiple metrics for feature selection and predictive modeling, outperforming individual criteria in Alzheimer's disease analysis.

A One-Shot Lossless Algorithm for Cross-Cohort Learning in Mixed-Outcomes Analysis

This paper introduces mixWAS, a one-shot, lossless algorithm for integrating distributed EHR datasets to identify cross-cohort genetic associations across multiple outcomes.

An Integrative Study to Investigate Sex-Specific Biomarkers in Bladder Cancer Patients

This study identifies sex-specific biomarkers in bladder cancer by analyzing genomic, expression, and clinical data from TCGA and GTEx, revealing distinct pathways and hub genes associated with male and female patients.

Using GPT-4 to write a scientific review article: a pilot evaluation study

This study evaluates GPT-4's ability to generate text, tables, and diagrams for biomedical review papers and suggests improvements for scientific use.

Predicting risk of Alzheimer's disease using polygenic risk scores developed for Parkinson's disease

This study evaluates the predictive power of polygenic risk scores for Alzheimer's disease and Parkinson's disease, finding that filtered scores for both diseases are strongly predictive, with AD scores better predicting PD than vice versa.

Improving Genetic Association Studies with a Novel Methodology that Unveils the Hidden Complexity of All-Cause Heart Failure

The paper introduces TRACE, a GWAS method that improves variance explained in All-Cause Heart Failure by accounting for phenotype heterogeneity and identifying gene-environment interactions.

Computational Framework for Statistical Epistasis Supports XOR Penetrance Function in a Living System

The paper introduces efficient algorithms for detecting statistical epistasis using varied interaction encodings and applies them to rat and mouse datasets to find evidence of epistasis in body mass index.

Automated quantitative trait locus analysis (AutoQTL)

AutoQTL automates QTL analysis and GWAS by applying machine learning to detect both additive and non-additive genetic effects in genetic data.

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets

The paper identifies epistatic SNP-SNP interactions in long-range LD across five diseases using UK Biobank and eMERGE data, finding associations involving conserved genes and essential genes.

LoFTK: a framework for fully automated calculation of predicted Loss-of-Function variants

LoFTK automatically predicts LoF variants and identifies genes affected by compound heterozygous LoF variants in genomes.

Polygenic risk vectors (PRV) improve genetic risk stratification for cardio-metabolic diseases

This paper introduces polygenic risk vectors (PRV) to improve genetic risk stratification for cardio-metabolic diseases by combining additive and dominant genetic effects, outperforming traditional polygenic risk scores.

Genetic analysis of coronary artery disease using tree-based automated machine learning informed by biology-based feature selection

This study uses TPOT to identify SNPs associated with coronary artery disease, finding 28 recurrent SNPs with functional relevance to known CAD genes and distinct clusters of predicted CAD cases.

Multiomics single-cell analysis of human pancreatic islets reveals novel cellular states in health and type 1 diabetes

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

The paper extends the TPOT AutoML tool to include covariate adjustments in biomedical data analysis, showing its utility in toxicogenomics and gene expression studies.

treeheatr: an R package for interpretable decision tree visualizations

treeheatr provides an R package for visualizing decision trees with heatmaps at leaf nodes to illustrate feature importance and model performance.

Detection of complex genetic architecture using two-locus population differentiation: modeling epistasis

This paper introduces a method to detect epistasis in complex traits by analyzing population differentiation at two loci, identifying novel gene interactions in pigmentation, schizophrenia, and preterm birth.

Learning from local to global - an efficient distributed algorithm for modeling time-to-event data

The paper introduces ODAC, a privacy-preserving distributed algorithm for multi-center Cox proportional hazard models, showing it matches pooled estimates and outperforms meta-analysis for rare events.

Genome wide-association study identifies novel loci in the Primary Open-Angle African American Glaucoma Genetics (POAAGG) study

The paper identifies a novel POAG-associated SNP in African Americans using a genome-wide association study.

Evaluation of phenotyping errors on polygenic risk score predictions

This study evaluates how phenotyping errors affect polygenic risk score (PRS) accuracy using eMERGE data, showing accuracy degradation depends on phenotype and error mechanism.

Scaling tree-based automated machine learning to biomedical big data with a dataset selector

The paper introduces two features to TPOT, a machine learning tool, to improve its scalability and efficiency for biomedical big data analysis.

CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature Analysis

CNNcon is a Docker-based tool for analyzing lung CT images to predict malignancy and extract features for further analysis.

Statistical Inference Relief (STIR) feature selection

The paper introduces STIR, a statistical inferential framework for Relief-based feature selection that accounts for variance in nearest neighbor distances to assess feature importance and significance in high-dimensional data.

runibic: a Bioconductor package for parallel row-based biclustering of gene expression data

The paper introduces a Bioconductor package called runibic that provides a parallel version of the UniBic biclustering algorithm for gene expression data, improving speed and memory efficiency.

Collective feature selection to identify crucial epistatic variants

The paper introduces a collective feature selection approach to identify epistatic variants by combining multiple methods, showing improved performance in detecting true positive interactions in both simulated and real BMI data.

Characterizing and Managing Missing Structured Data in Electronic Health Records

The paper evaluates imputation methods for missing structured data in EHRs using clinical lab measures from 602,366 patients, showing some methods perform well while others are unsuitable for multiple imputation.