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Soufiane Mourragui's Projects

balistic-project icon balistic-project

I have designed this software for a project in my last year of Bachelor at Mines ParisTech. It is called the "Balistic Game" : two war-engines are confronting to one another and the first able to destroy the other wins. It has been designed in Java

bonito icon bonito

A PyTorch Basecaller for Oxford Nanopore Reads

cellrank icon cellrank

CellRank for directed single-cell fate mapping

dnascent icon dnascent

Software for detecting regions of BrdU and EdU incorporation in Oxford Nanopore reads.

paradoxical_activation_scrnaseq icon paradoxical_activation_scrnaseq

Analysis pipeline for the scRNA-seq data presented in "Paradoxical activation of oncogenic signaling as a cancer treatment strategy"

preparatory-class-projects icon preparatory-class-projects

You will find there my personnal projects made during my two years of prepatory class. The first one simulates a Black-Jack game and answers the question : are counting cards systems worth it ? (the answer is yes). The second one is more physics oriented. It simulates graphically and analytically a chemical reaction of diffusion known as Belouzov-Zhabothinsky reaction. This model uses the works of the Oregonator project.

pydeseq2 icon pydeseq2

A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.

remora icon remora

Methylation/modified base calling separated from basecalling.

research-project-diffusion-network icon research-project-diffusion-network

During my first year of master I have developed a software that enable to uncover a network given the knowledge of cascades. I adapted the work of other researchers for two cases : one single infection (cf. J.Leskovec's work) and plural infections.

scvi-tools icon scvi-tools

Deep probabilistic analysis of single-cell omics data

tombo icon tombo

Tombo is a suite of tools primarily for the identification of modified nucleotides from raw nanopore sequencing data.

transferlearning icon transferlearning

Resources and codes about transfer learning and domain adaptation--迁移学习

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