Aaron S. Meyer

This study integrates time-series cytokine and liver function data from liver transplant patients to identify immunological signatures linked to disease etiology, injury, and CD4+ T cell polarization affecting allograft survival.

This study identifies tumor-binding autoantibodies in ovarian cancer patients that fail to engage Fc{gamma}RIIIa, impairing antibody-dependent cellular cytotoxicity.

The paper introduces CCC-RISE, a method to analyze cell-cell communication at single-cell resolution across conditions, identifying disease-relevant signaling programs and subpopulations.

RISE, an adaptation of PARAFAC2, enables dimensionality reduction and analysis of single-cell RNA-seq data across experimental conditions by separating condition-specific effects from cell-to-cell variation.

The paper profiles cytokine signaling responses in breast cancer patients and finds coordinated dysregulation across pathways and cell types, suggesting immune cell reprogramming and immunosuppression.

This study compares censored least squares to other methods for imputing missing values in tensor factorization of biological datasets, finding censored least squares to be more accurate and efficient.

This paper introduces a multivalent binding model to infer antibody structural features from systems serology data, revealing insights into vaccine efficacy and disease progression.

The paper uses phosphosite perturbations to identify signaling pathways driving AXL-mediated erlotinib resistance in lung cancer.

The paper introduces DDMC, a clustering strategy for phosphoproteomic data that groups phosphopeptides by phosphorylation abundance and sequence motifs, improving modeling and interpretation of kinase signaling in lung cancer.

This paper introduces a general model for predicting multivalent ligand binding to cells with heterogeneous receptors and multiple surface ligands, enabling large-scale binding predictions.

This paper systematically evaluates IL-2-Fc fusions to enhance Treg selectivity by exploring valency, orientation, and binding kinetics, identifying multivalency and asymmetry as key factors for improved specificity.

This paper introduces a lineage tree-based hidden Markov model to quantify cellular heterogeneity and plasticity using single-cell lineage data, showing it can classify distinct phenotypic states and drug responses in cancer and non-cancer cell populations.

This study shows that ZIKV infects neural progenitors, astrocytes, and microglia-like cells, with microglia acting as viral reservoirs and facilitating infection of the fetal brain.