A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolution
-
Updated
Oct 1, 2024 - Jupyter Notebook
A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolution
A bunch of PK/PD patient models with various diseases for open-/closed-loop control systems
This project uses machine learning to detect brain tumors early. Different models are trained on metabolite data to classify healthy and carcinoma-afflicted individuals. The top-performing EvoHDTree model is enhanced with the ADASYN up-sampling algorithm to accurately distinguish between malignant and non-malignant tumors.
Glioblastoma tumour classfication and tumour grade segmentattion using U-NET CNN
A deep learning model to detect tumors in the given MRI images.
Bamgineer: Introduction of simulated allele-specific copy number variants into exome and targeted sequence data sets
Implementation of tumoral growth using a discrete celullar automata, based on Dustin D.Phan work
MATLAB scripts for the paper "Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer"
A simple tumour classifier based on microRNA expression profiles
Add a description, image, and links to the tumour topic page so that developers can more easily learn about it.
To associate your repository with the tumour topic, visit your repo's landing page and select "manage topics."