Pengtao Dang

This paper introduces mmFEA, a kinetic-aware framework for estimating metabolic flux changes from transcriptomics and metabolomics data, showing improved accuracy over baseline methods.

MetaKnogic-Alpha transforms biomedical literature into a hyper-relational knowledge base for metabolic reasoning, achieving high mechanistic accuracy through automated synthesis and biochemical grounding.

This paper develops TDMA, a transcriptomics-based model to estimate DNA methylation activity and epigenetic aging from bulk, single-cell, and spatial transcriptomics data.

S3R models spatially varying associations in spatial transcriptomics data by estimating location-specific coefficients with structured sparsity and smoothness penalties, showing effectiveness in synthetic and real datasets.

This study uses TCGA transcriptomics data to analyze metabolic shifts in 11 cancer subtypes and their adjacent normal tissues, revealing patterns in glucose uptake, glycolysis, TCA cycle, and glutaminolysis.

This paper introduces scFEA, a graph neural network model to estimate cell-wise metabolic flux from scRNA-seq data, validated with matched metabolomics data and applied to multiple datasets.

This study shows that hPSC-derived RGCs with the OPTN(E50K) mutation display neurodegenerative phenotypes, providing insights into glaucoma mechanisms.