Urszula Czerwinska (UrszulaCzerwinska)

UrszulaCzerwinska

Geek Repo

Company:Cour de Cassation

Location:Paris

Home Page:urszulaczerwinska.github.io

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Urszula Czerwinska's repositories

DeconICA

Deconvolution of transcriptome through Immune Component Analysis

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awesome-single-cell

List of software packages for single-cell data analysis, including RNA-seq, ATAC-seq, etc.

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FromSciencetoDataScience

materials from "From Science to Data Science seminars"

UCzPhDThesis

Github for bookdown project

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UrszulaCzerwinska.github.io

Check out the section about me, blog is coming soon

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molecular_map_scores

script used to compute scores of cell groups in molecular maps for publication in Nature Communiocations

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AIV_Matlab

Project of modelling MinA and MinD - cell division

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amritr

Amrit's personal data analyses functions, concepts and explorations

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bookdown-crc

A minimal example of using bookdown to write a book for Chapman & Hall

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CURIE2015

My_R_SCRIPTS

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CURIE2016_ICA

deconvolution of mixed samples

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DeDaL

DeDaL (Data-Driven network Layout) is a Cytoscape 3.0 [1] app developed by the Computational Systems Biology of Cancer group in Bioinformatics Laboratory of Institut Curie (Paris).

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duplicate_detector

duplicate-dtector-cli

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Mines_ML

class http://members.cbio.mines-paristech.fr/~jvert/svn/lsml/lsml17/

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mock_project_regression

this is a small ML regression demo in python

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PaddleOCR

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

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Pets

Pets kaggle competiton https://www.kaggle.com/c/petfinder-adoption-prediction

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QuantumClone

Clonal reconstruction from HTS data

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R_viz_course

http://urszulaczerwinska.github.io/works/TP_R this is a course for bachelor students that never programmed in R

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rr-r-publication

Reproducible Research Workshop Demo repository for publication writing in R

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sequential_sentence_classification

https://arxiv.org/pdf/1909.04054

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XAIbookchapter

The code for the book "Data Science in Tourism" chapter Interpretability of Machine Learning models

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