Austin W.T. Chiang

LeGenD predicts N-glycan structures and their abundance using lectin-binding patterns and AI, trained on glycoprofiles from recombinant proteins.

The paper investigates the phenotypic and transcriptional effects of glycoengineering in CHO cells, identifying distinct clusters and molecular mechanisms associated with different glycosyltransferase modifications.

This study shows that PDE10A inhibition with ADT-030 suppresses tumor growth by disrupting MAPK signaling and enhancing antitumor immunity through immune cell modulation.

This study shows that glycoengineering of HCV E2 glycoprotein affects antigen biochemical properties and immune response, with some glycans improving neutralization potency.

iLipidome analyzes lipidomics data using biosynthetic networks to improve statistical power and interpretability, identifying genetic causes of lipid changes and potential therapeutic targets.

The paper identifies a relationship between protein structure and glycan structure, introducing a glycoimpact matrix to predict glycosylation patterns and validate it through disease variants and engineered antibodies.

This paper uses a Boltzmann model to predict glycan structures from lectin binding data, achieving 88% accuracy for N-glycans and 87% for O-glycans in a test set.

LipidSIM infers lipid biosynthesis perturbations from lipidomics data using a low-parameter Markov model, demonstrating its utility in studying disease-related lipidome changes.

GlyCompareCT preprocesses glycomics data to address sparsity and non-independence, improving statistical power for downstream analysis.

The paper constructs an HMO biosynthesis network using multi-omics integration to predict glycosyltransferases and propose candidate genes for HMO synthesis pathways.

The paper introduces a framework to predict how changes in omics data affect metabolic functions in mammalian cells using curated metabolic tasks and genome-scale networks.

This paper introduces BalanceIT, a method using genetic algorithms to balance clinical factors in RNA-Seq studies, improving differential gene expression and pathway analysis in autism research.

This paper improves ASD outcome prediction from gene expression using transfer learning, achieving a 7.8% AUROC improvement over a naive model.

The paper connects Alzheimer's disease genetics to amyloid formation by analyzing secretory pathway dysregulation in AD brains.

This paper extends Linear Code to represent reaction rules in glycobiology, aiming to improve consistency and interoperability in glycan synthesis models.

This study identifies two groups of ASD risk genes—broadly-expressed and brain-specific—that disrupt sequential stages of neurodevelopment through distinct regulatory networks.

The paper introduces GlyCompare, a glycomic analysis method that addresses sparsity and non-independence by leveraging biosynthetic pathways, revealing biological insights in glycoprofiles and human milk oligosaccharides.

This study improves context-specific metabolic models by integrating data-inferred cell functions, reducing model variability across algorithms and better capturing biological variability.