Precision Oncology | Jennifer Yu

Longitudinal AI Models Predict Cancer Adverse Events

Mon, 26 Oct 2026 00:00:00 +0000

Presented at ISMB 2026 (Best Oral Presentation).

Generalist large language models complement tailor-made predictors for tumor genomics interpretation

Fri, 22 May 2026 00:00:00 +0000

Allostatic load connects tumor genomics, disease trajectory, and pre-diagnosis wearable activity

Fri, 03 Apr 2026 00:00:00 +0000

Large language models for tumor genomic interpretation

Fri, 03 Apr 2026 00:00:00 +0000

Also presented at ISMB 2026.

Generalist Large Language Models for Tumor Genomics Interpretation

Wed, 01 Jan 2025 00:00:00 +0000

Tumor genomic profiling produces results that must be interpreted before they can guide care: what is the most likely cancer type, is a variant somatic or the result of clonal hematopoiesis (CHIP), and is it likely to be oncogenic? Specialized, tailor-made predictors exist for each of these questions, but they are trained on a single institution’s data and labels and can degrade when deployed elsewhere.

In this project, which I lead as part of my PhD work with Dr. Quaid Morris and Dr. Justin Jee at Memorial Sloan Kettering Cancer Center, we benchmark zero-shot generalist large language models against supervised baselines on cancer-type, somatic-vs-CHIP and variant-oncogenicity classification using more than 100,000 samples from 19 institutions. We find that generalist LLMs and tailor-made predictors are complementary: LLM–specialist ensembles raise cross-institution accuracy by up to 55%. The work was presented at the AACR Annual Meeting 2026 and ISMB 2026, and a preprint is available on bioRxiv.

Foundation Models for Longitudinal Clinical Time Series in Oncology

Sun, 01 Sep 2024 00:00:00 +0000

Routine clinical care generates long, irregular time series of laboratory values and vital signs for every patient. For patients with cancer, these measurements carry early signals of treatment toxicities and other adverse events, but they are rarely modeled jointly and longitudinally.

As part of my PhD work with Dr. Quaid Morris and Dr. Justin Jee at Memorial Sloan Kettering Cancer Center, I contribute to SPARC, a transformer-based foundation model pretrained on more than 100 million longitudinal lab and vital-sign measurements from 224,047 patients with cancer. The pretrained model is fine-tuned to predict treatment toxicities, adverse events and mortality, and generalizes to external cancer centers. The project was presented at the NeurIPS 2025 Workshop on Learning from Time Series for Health (TS4H) and at ISMB 2026, where it received a Best Oral Presentation award; a preprint, Longitudinal AI Models Predict Cancer Adverse Events, is available on medRxiv.