Bamgineer: Introduction of simulated allele-specific copy number variants into exome and targeted sequence data sets

A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolution

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

Grouped Read Testing

ML Algorithms for Classifying Tissue According to Methylation and Gene Expression

Implementation of tumoral growth using a discrete celullar automata, based on Dustin D.Phan work

Glioblastoma tumour classfication and tumour grade segmentattion using U-NET CNN

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.

A deep learning model to detect tumors in the given MRI images.