Philip Payne

Batch Effect Correction to Enhance Utility of a Publicly Available ALS Proteomics Dataset

The paper corrects batch effects in the Answer ALS proteomics dataset using BDCs and ComBat, improving data utility while preserving biological variability.

CellTosg2Sequence: A Unified Text-Omics-Signaling-Graph Large Language Model for Single-Cell Analysis

CellTosg2Sequence integrates biomedical text and signaling graphs into a language model for single-cell analysis, improving performance over existing models.

BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation

BioMedGraphica integrates biomedical knowledge into a unified graph and introduces a Textual-Numeric Graph structure for multi-omics analysis.

Interpreting Omics Data Analysis with Large Language Models for Disease Target and Drug Discovery

This paper introduces a framework combining large language models with omics data analysis to prioritize disease targets and drug strategies, validated in Alzheimer's and pancreatic cancer.

SnakeAltPromoter Facilitates Differential Alternative Promoter Analysis

SnakeAltPromoter provides a reproducible framework for differential alternative promoter analysis using RNA-seq, benchmarked against CAGE data and scalable for large datasets.

OmniCellAgent: An AI Scientist for Omic-Driven Scientific Discovery

OmniCellAgent autonomously retrieves and analyzes scRNA-seq datasets to generate structured reports and hypotheses across disease settings.

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers

This study uses multimodal normative modeling on ATN biomarkers to analyze Alzheimer Disease heterogeneity, finding greater intra-group variation in more severe stages and a correlation between disease severity index and cognitive decline.

M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis

M3NetFlow is a multi-scale multi-hop graph model for multi-omic data analysis that ranks targets and infers signaling pathways, achieving high accuracy in drug response and Alzheimer's disease studies.

Multimodal normative modeling in Alzheimer Disease with introspective variational autoencoders

This paper introduces a multimodal introspective VAE for Alzheimer's Disease normative modeling, improving reconstruction of healthy controls and outlier detection in neuroimaging data.

Examining heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles

This study uses unsupervised clustering on longitudinal cognitive data to identify subtypes of dementia, finding that mild dementia subtypes are more heterogeneous in progression.

mosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data

This study introduces mosGraphFlow, a graph AI model for analyzing multi-omic data to identify AD-associated biomarkers and signaling pathways, achieving high classification accuracy.

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI

mosGraphGPT is a foundation model for multi-omic signaling graphs trained on cancer data and fine-tuned for Alzheimer's Disease, improving classification accuracy and interpretability.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications

PathFinder infers multi-cell signaling pathways and communications using a graph transformer model, identifying novel interactions in AD mouse models.

SC2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data

SC2MeNetDrug is a tool that identifies cell-cell signaling targets and predicts drugs using scRNA-seq data to analyze disease microenvironments.

mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development

mosGraphGen generates multi-omics signaling graphs to enable integrative and interpretable graph AI model development by converting multi-omics data into biologically meaningful graphs.

Leveraging GPT-4 for Identifying Cancer Phenotypes in Electronic Health Records: A Performance Comparison between GPT-4, GPT-3.5-turbo, Flan-T5 and spaCy's Rule-based & Machine Learning-based methods

This paper compares GPT-4, GPT-3.5-turbo, Flan-T5, and spaCy-based methods for identifying cancer phenotypes from EHRs, finding GPT-4 achieves higher precision, recall, and F1 scores.

Decipher macrophage-fibroblast-cardiomyocyte signaling interactions associated with heart failure using deep graph neural network models and single-cell RNA-seq data

This study uses deep learning models and single-cell RNA-seq data to identify molecular targets and signaling pathways involved in macrophage-fibroblast-cardiomyocyte interactions in heart failure.

Loss of estrogen unleashing neuro-inflammation increases the risk of Alzheimer's disease in women

This study identifies estrogen's role in suppressing neuro-inflammation and links its loss to increased Alzheimer's disease risk in women through systems pharmacology and signaling network analysis.

Multi-omics data integration via novel interpretable k-hop graph attention network for signaling network inference

This study introduces a novel interpretable k-hop graph attention network to integrate multi-omics data and infer essential targets and signaling networks, showing improved performance over existing models.

Weakly activated core inflammation pathways were identified as a central signaling mechanism contributing to the chronic neurodegeneration in Alzheimer's disease

Mining signaling flow to interpret mechanisms of synergy of drug combinations using deep graph neural networks

The paper introduces DeepSignalingFlow, a deep learning model that predicts drug combination synergy and interprets the mechanisms of synergy using graph neural networks.

Signaling interaction link prediction using deep graph neural networks integrating protein-protein interactions and omics data

The paper introduces a deep learning model to predict signaling cascades by integrating PPI data and omics data, validated using curated KEGG pathways.

Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model

This study introduces DeepSignalingSynergy, a simplified deep learning model that predicts tumor cell response to drug combinations using cancer signaling pathways, showing feasibility with NCI ALMANAC data.

CytoGPS: A Web-Enabled Karyotype Analysis Tool for Cytogenetics

CytoGPS converts ISCN karyotype data into a machine-readable format and binary LGF model for downstream computational analysis.