agrigis Goto Github PK

2023_rlouiset_sepvae

agrigis.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

baobab

https://baobablab.github.io/baobab/

brain_age

A deep learning model to predict individual brain ages from MRI datasets

brain_age_deep

Predict age from brain anatomy measures of Grey Matter (GM) volumes using Deep Learning.

brain_age_with_site_removal

Age prediction with site-effect removal: A challenge on the openBHB dataset that aims to i) predict age from derived 3D T1w anatomical MRI data while ii) removing site/scanner information from the learned representation.

brain_segmentation

brainomics2

Logic CubicWeb cube

bredala

Easy to use pure-python caller signature and profiler.

capsul

Collaborative Analysis Platform : Simple, Unifying, Lean

casper

deepcluster

Deep Clustering for Unsupervised Learning of Visual Features

deepimv

A Variational Information Bottleneck Approach to Multi-Omics Data Integration

deeplearning-biology

A list of deep learning implementations in biology

deepmreye

Main model and preprocessing code

dsanet

Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".

exerciceweb

job CATI

gadgetron

gene-graph-conv

Towards Gene Expression Convolutions using Gene Interaction Graphs

h2_multi

heritability analysis with multidimensional matrices

hopla

imaging-genetics

A Generative Discriminative Framework that Integrates Imaging, Genetic, andDiagnosis Data into Coupled Low Dimensional Space

jupyterhub-deploy-docker

Reference deployment of JupyterHub with docker

jupyterhub-docker

A configuration for a JupyterHub+DockerSpawner+OAuth2 server with Traefik proxy, based on docker-compose

mfsda_python

Multivariate Functional Shape Data Analysis in Python (MFSDA_Python) is a Python based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical, biological variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.

mgn-net

MGN-Net: A novel Graph Neural Network for integrating heterogenous graph population derived from multiple sources.

nilearn

Machine learning for NeuroImaging in Python

nipype

Workflows and interfaces for neuroimaging packages

openmorph

Curated list of open-access databases with human structural MRI data

papers

Summaries of machine learning papers