Francesco Carli
@mrfcharles.bsky.social
PhD student in Artificial Intelligence @Scuola Normale Superiore, Pisa, Italy. Moving to EMBL-EBI, Cambridge, UK from 2025
Pinned
Francesco Carli
@mrfcharles.bsky.social
· Feb 16
Learning and actioning general principles of cancer cell drug sensitivity - Nature Communications
Potential anti-tumor therapies remain to be discovered in cancer cell line high-throughput screening datasets. Here, the authors develop a machine learning approach to infer cancer cell drug sensitivi...
www.nature.com
🎉Our latest paper is out in Nature Communications: "Learning and Actioning General Principles of Cancer Cell Drug Sensitivity" 📄🔍
We present an interpretable ML pipeline that predicts drug sensitivity in cancer cell lines, leveraging large-scale pharmacogenomics datasets for actionable insights. 🧬💊
We present an interpretable ML pipeline that predicts drug sensitivity in cancer cell lines, leveraging large-scale pharmacogenomics datasets for actionable insights. 🧬💊
🎉Our latest paper is out in Nature Communications: "Learning and Actioning General Principles of Cancer Cell Drug Sensitivity" 📄🔍
We present an interpretable ML pipeline that predicts drug sensitivity in cancer cell lines, leveraging large-scale pharmacogenomics datasets for actionable insights. 🧬💊
We present an interpretable ML pipeline that predicts drug sensitivity in cancer cell lines, leveraging large-scale pharmacogenomics datasets for actionable insights. 🧬💊
Learning and actioning general principles of cancer cell drug sensitivity - Nature Communications
Potential anti-tumor therapies remain to be discovered in cancer cell line high-throughput screening datasets. Here, the authors develop a machine learning approach to infer cancer cell drug sensitivi...
www.nature.com
February 16, 2025 at 5:40 PM
🎉Our latest paper is out in Nature Communications: "Learning and Actioning General Principles of Cancer Cell Drug Sensitivity" 📄🔍
We present an interpretable ML pipeline that predicts drug sensitivity in cancer cell lines, leveraging large-scale pharmacogenomics datasets for actionable insights. 🧬💊
We present an interpretable ML pipeline that predicts drug sensitivity in cancer cell lines, leveraging large-scale pharmacogenomics datasets for actionable insights. 🧬💊
🧵 A huge thanks to the creators of umap-learn for their work on UMAP! While trying to get parametric UMAP to work for my project, I ran into some challenges, so I decided to dive in and build my own PyTorch-based version from scratch. You can check it up here github.com/mr-fcharles/...
GitHub - mr-fcharles/parametric_umap: A PyTorch implementation of Parametric UMAP (Uniform Manifold Approximation and Projection) for learning low-dimensional parametric embeddings of high-dimensional...
A PyTorch implementation of Parametric UMAP (Uniform Manifold Approximation and Projection) for learning low-dimensional parametric embeddings of high-dimensional data - mr-fcharles/parametric_umap
github.com
November 21, 2024 at 3:57 PM
🧵 A huge thanks to the creators of umap-learn for their work on UMAP! While trying to get parametric UMAP to work for my project, I ran into some challenges, so I decided to dive in and build my own PyTorch-based version from scratch. You can check it up here github.com/mr-fcharles/...