dpaysan Goto Github PK

adamw-pytorch

Implementation and experiments for AdamW on Pytorch

causaldag

Python package for the creation, manipulation, and learning of Causal DAGs

chromark

Code for the manuscript: "Single-cell imaging-AI based chromatin biomarkers for diagnosis and therapy evaluation in tumor patients using liquid biopsies". Code was developed by Daniel Paysan

chuv_collaboration

cross-modal-autoencoders

dpaysan

Config files for my GitHub profile.

germinal_center

Python code for the chromatin state analyses of the paper

hsmmlearn

A library for hidden semi-Markov models with explicit durations

image2reg

Code for the paper "Image2Reg: Linking Chromatin Images to Gene Regulation using Genetic Perturbation Screens"

imageaeot

intro2ml

Code of the projects of the Introduction to Machine Learning course 2019 at ETHZ.

jump_datasets

Images and other data from the JUMP Cell Painting Consortium

material

Material for course STA426 at UZH, Fall Semester 2020

nmco-image-features

Nuclear Morphology and Chromatin Organization Features

node2vec_stability

pybasicbayes

Custom version of the pybasicbayes package edited to be used in the context of surgical activity recognition using HSMMs.

pyhsmm

Customized version of the original software package to be used for surgical activity recognition using HSMMs.

pyscenic

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

scvae

Deep learning for single-cell transcript counts

simpleitk-notebooks

Jupyter notebooks for learning how to use SimpleITK

st-net

Deep learning on histopathology images.

sta426dp

Repository for the course work of the module STA426DP

unet-nuclei

Segmentation of fluorescently labelled nuclei using a pre-trained Unet

unet_nuclei_segmentation