Anna Konstorum

The paper introduces metrics for assessing tensor decomposition quality in immune profiling data using a dictionary learning framework.

The paper benchmarks 49 algorithms for predicting B. pertussis vaccination outcomes, finding that purpose-built models outperform literature-based ones due to better handling of nonlinearities and data preprocessing.

The paper introduces nipalsMCIA, a tool for multi-omics dimensionality reduction that improves speed and flexibility over existing methods for single-cell data.

This study identifies immune programs linked to COVID-19 severity and mortality through longitudinal multi-omics analysis of 1,152 hospitalized patients.

This paper introduces a supervised Bayesian factor model to improve multi-omics signature discovery by integrating dimensionality reduction and predictive modeling.

The paper evaluates computational models predicting immune responses to Pertussis Booster vaccinations using a new community resource, finding that existing models perform poorly and new models show variable performance based on features and strategies.

The paper compares dimension reduction techniques on mass cytometry data, finding t-SNE and Diffusion Maps best for preserving cell relationships and providing informative visualizations.

This review discusses mathematical and computational models addressing challenges in cancer immunotherapy, including tumor classification, treatment scheduling, and combination therapy design.