Patrick Holec (BIRNBAUM LAB)

A Bayesian Framework for High-throughput T Cell Receptor Pairing

The study of T cell receptor repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for T cell receptors. We describe a novel analytical approach to an emerging immune repertoire sequencing method, improving the resolving power of this low-cost technology. With this advance, we intend to leverage its scalability as a scientific and diagnostic tool.

Divya Ramamoorthy (FRAENKEL LAB)

Deep Phenotyping of ALS Patients Using Multi-modal Data

The heterogeneity of Amyotrophic Lateral Sclerosis is poorly understood, despite its importance in developing effective therapeutics. Through paired molecular and clinical data, we aim to characterize subgroups of patients and find biomarkers that correlate with disease progression. We have developed machine learning tools to identify groups of patients that follow distinct patterns of muscle strength and cognitive decline, and genomic mutations that correlate with clinical phenotype.