Excited to share Dr. Elkin's NetFlow manuscript — a framework for constructing interpretable graph representations of high-dimensional biomedical data. It captures both clustering and continuous variation to reveal biologically meaningful structure. #AI#Oncology#Bioinformatics
Excited to share Dr. Elkin's NetFlow manuscript — a framework for constructing interpretable graph representations of high-dimensional biomedical data. It captures both clustering and continuous variation to reveal biologically meaningful structure. #AI#Oncology#Bioinformatics
(1/2) Lung cancer can co-opt genes that normally help a fetus develop and evade the mother’s immune system, according to research from Dr. Jung Hun Oh.
And while these pregnancy-specific glycoproteins (PSGs) can get activated in the cancers of both men & women, female patients had poorer outcomes.
(1/2) Lung cancer can co-opt genes that normally help a fetus develop and evade the mother’s immune system, according to research from Dr. Jung Hun Oh.
And while these pregnancy-specific glycoproteins (PSGs) can get activated in the cancers of both men & women, female patients had poorer outcomes.
🚀 Just published in Bioinformatics! Introducing ORCO: Ollivier-Ricci Curvature-Omics, a python package for analyzing robustness in biological systems. A 🧵 on what it is, why it matters, and how you can use it. With @joedeasy.bsky.social and the great team at @mskcancercenter.bsky.social
March 14, 2025 at 3:20 PM
🚀 Just published in Bioinformatics! Introducing ORCO: Ollivier-Ricci Curvature-Omics, a python package for analyzing robustness in biological systems. A 🧵 on what it is, why it matters, and how you can use it. With @joedeasy.bsky.social and the great team at @mskcancercenter.bsky.social
ORCO: Ollivier-Ricci Curvature-Omics: an unsupervised method for analyzing robustness in biological systems https://www.biorxiv.org/content/10.1101/2024.10.06.616915v1
ORCO: Ollivier-Ricci Curvature-Omics: an unsupervised method for analyzing robustness in biological systems https://www.biorxiv.org/content/10.1101/2024.10.06.616915v1