oubounyt Goto Github PK
Name: Mhaned Oubounyt
Type: User
Company: University of Hamburg
Bio: Machine learning solution for bioinformatics challenges.
Location: Hamburg
Blog: https://www.cosy.bio/
Mhaned Oubounyt's Projects
deepromoter
Pytorch implementation of DeePromoter Active sequence detection for promoter(DNA subsequence regulates transcription initiation of the gene by controlling the binding of RNA polymerase)
drugstone
The python package for the https://drugst.one/ platform.
eukaryotic-promoter-recognition-using-cnn
method for Eukaryotic promoter prediction based on CNN model
example-publish-pypi
Example Python package to demonstrate how to publish packages on PyPI
heart_visium
Ablation of Vitamin D Signaling in Cardiomyocytes Leads to Functional Impairment and Stimulation of Pro-Inflammatory and Pro-Fibrotic Gene Regulatory Networks in a Left Ventricular Hypertrophy Model in Mice
inference_of_gene_regulatory_networks
4th Semester Bioinformatics internship
methods-of-clustering-single-cell-rna-sequencing-data
Single-cell sequencing is a novel technology to define intercellular heterogeneity, rare cell types, cell genealogies or disease evolution based on profiling thousands of cells in parallel. Identification of cellular subpopulations from heterogeneous populations of cells could be done by data clustering.
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.
retenmod
retention models to forecast churn
sc-networks
My internship working on single cell RNA data and creating networks to find genes that are expressed differently in e.g. healthy and diseased cells.
scanet
SCANet is a python package that incorporates the inference of gene co-expression networks from single-cell gene expression data
scanpy
Single-cell analysis in Python. Scales to >1M cells.
scenicprotocol
A scalable SCENIC workflow for single-cell gene regulatory network analysis
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