tumour

There are 0 repository under tumour topic.

pughlab / bamgineer
Bamgineer: Introduction of simulated allele-specific copy number variants into exome and targeted sequence data sets
copy number variation circulating tumour dna simulation python docker
Language:Python 37
CompEpigen / methylbert
A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolution
bert deconvolution language-model methylation tumour
Language:Jupyter Notebook 11
paoloinglese / 3D_DESI
MATLAB scripts for the paper "Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer"
clustering dbscan-clustering desi-msi dimensionality-reduction imaging mass-spectrometry optics optics-clustering parametric-tsne tsne tumour
Language:Matlab 1
parttimenerd / tumour_classifier
A simple tumour classifier based on microRNA expression profiles
machine-learning tumour classification
Language:Python 1
coloal / Discrete-CA-for-tumoral-growth-implementation
Implementation of tumoral growth using a discrete celullar automata, based on Dustin D.Phan work
parallel simulation tumour tumour-growth
Language:Java 0
ironghost007 / Glioblastoma-tumour-classification-and-segmentation
Glioblastoma tumour classfication and tumour grade segmentattion using U-NET CNN
brain-tumor-segmentation brain-tumour-detection classification machine-learning python unet-image-segmentation glioblastoma tumour brats-dataset pca-analysis random-forest xgboost
Language:Jupyter Notebook 0
LordGenome / amplivar
Grouped Read Testing
tumour amplicon ngs
Language:Perl 0
Sinha199 / BrainTD
A deep learning model to detect tumors in the given MRI images.
brain-mri cnn deep-learning google-colab hacktoberfest hacktoberfest2020 kaggle-dataset keras neuroscience tumor tumour
Language:Jupyter Notebook 0
blid11 / Group9
ML Algorithms for Classifying Tissue According to Methylation and Gene Expression
svm tumour
Language:Jupyter Notebook
mudit2004 / Enhancing-Machine-Learning-Models-for-Early-Brain-Tumor-Diagnosis
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.
brain deep-learning machine-learning python streamlit tumour

tumour

pughlab / bamgineer

CompEpigen / methylbert

paoloinglese / 3D_DESI

parttimenerd / tumour_classifier

coloal / Discrete-CA-for-tumoral-growth-implementation

ironghost007 / Glioblastoma-tumour-classification-and-segmentation

LordGenome / amplivar

Sinha199 / BrainTD

blid11 / Group9

mudit2004 / Enhancing-Machine-Learning-Models-for-Early-Brain-Tumor-Diagnosis