Portrait de Stephanie Zandee

Stephanie Zandee

Collaborateur·rice de recherche - McGill (assistant professor)
Superviseur⋅e principal⋅e
Guy Wolf
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage automatique médical
Apprentissage multimodal
Biologie computationnelle
Site web
Google Scholar

Publications

Gaining Biological Insights through Supervised Data Visualization
Jake S. Rhodes
Adrien Aumon
Sacha Morin
Marc Girard
Catherine Larochelle
Boaz Lahav
Elsa Brunet-Ratnasingham
Amélie Pagliuzza
Lorie Marchitto
Wei Zhang
Adele Cutler
F. Grand'Maison
Anhong Zhou
Andrés Finzi
Nicolas Chomont
Daniel E. Kaufmann
Stephanie Zandee
Alexandre Prat
Guy Wolf
Kevin R. Moon
Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHAT… (voir plus)E, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE’s prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.
2024-01-21
bioRxiv (prépublication)
doi.org
Gaining Biological Insights through Supervised Data Visualization
Jake S. Rhodes
Adrien Aumon
Sacha Morin
Marc Girard
Catherine Larochelle
Elsa Brunet-Ratnasingham
Amélie Pagliuzza
Lorie Marchitto
Wei Zhang
Adele Cutler
F. Grand'Maison
Anhong Zhou
Andrés Finzi
Nicolas Chomont
Daniel E. Kaufmann
Stephanie Zandee
Alexandre Prat
Guy Wolf
Kevin R. Moon
Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHAT… (voir plus)E, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE’s prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.
2024-01-21
bioRxiv (prépublication)
doi.org