prazg Goto Github PK

combat_rna

Python code for running pycombat library to harmonise gene level fpkm values from RNA seq data

cptac

Python packaging for CPTAC data

deep-features-nifti-network-den

This repository defines a Convolutional Neural Network (CNN) model that extracts deep features from Nifti MRI files of segmented glioblastoma tumours

dicomdata

This repository provides you with python code snippet on extracting Metadata of Dicom files including Magnetic field strength, study date and manufacturer.

ecotyper

EcoTyper is a machine learning framework for large-scale identification of cell states and cellular ecosystems from gene expression data.

gbm-data-longitudinal

This repository contains code used to prepare the LUMIERE Glioblastoma dataset.

gbmdeconvoluter

A Deconvolution Tool For Glioblastoma (GBM) Datasets

hd-bet

Automated deep-learning based brain extraction applicable to a broad range of MRI sequence types and with robust performance even in the presence of pathology or treatment-induced tissue alterations.

hd-glio

Automated deep-learning based brain tumor segmentation on MRI

metadata_bam

This is a library of codes for extracting and saving metadata obtained from a aligned BAM file.

neurocombat_radiomics

This repository provides code in jupyter notebook on application of neuroCombat package for radiomics data extracted from segmented MRI brain images

nnunet

pca_radiomics

Principal Component Analysis (PCA) on a dataset of radiomics features

pyradiomics

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics

tcellextrect

tools-to-design-or-visualize-architecture-of-neural-network

Tools to Design or Visualize Architecture of Neural Network

tractseg

Automatic White Matter Bundle Segmentation

unstranded-to-gene_level-fpkm

This repository has python code within Jupyter Notebook to convert unstranded fpkm values of RNA sequecing data to stranded fpkm values and then to gene-level fpkm values to standardize the data obtained from multiple datasets

xcell

Cell types enrichment analysis