Allison Creason

GLEAM provides a web-based toolkit for accessible and rigorous machine learning analyses in biomedical research, producing accurate models across three tasks.

The paper identifies resistance mechanisms to KRAS-MEK pathway targeting in PDAC by analyzing clinical trial data and multi-omics profiles, showing that KRAS/MYC co-gain correlates with resistance and that co-targeting these genes is synergistic.

This study examines how the spatial organization of the tumor-immune microenvironment changes during treatment in ER-positive breast cancer and links these changes to clinical response.

COEXIST integrates serial multiplexed tissue images at the single-cell level by combining molecular profiles with spatial information to improve profiling and platform validation.

This paper imputes single-cell protein abundance in multiplex tissue imaging data from a breast cancer cohort using machine learning, showing improved accuracy and biological relevance.

This study creates a multimodal tumor atlas to analyze how metastatic HR+ breast cancers adapt to CDK4/6 inhibitors, revealing mechanisms like mTORC1 activation and immune microenvironment changes.

The paper discusses challenges in digital image analysis of cancerous tissues and outlines solutions developed during a 2022 hackathon.

Galaxy-ME is a web-based software hub for analyzing and visualizing multiplex tissue imaging datasets, demonstrating its utility on normal and cancerous tissue data.

MCMICRO processes multiplexed tissue images into single-cell data, demonstrating its versatility across different tissues and imaging platforms.

The paper introduces Galaxy-ML, a web-based platform for reproducible and scalable supervised machine learning in biomedicine, extending the Galaxy computational workbench.