Workflows
What is a Workflow?Filters
📄 Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen healthcare settings
This repository contains the code used for the experiments in the paper:
Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen healthcare settings by Diane Duroux, Paul P. Meyer, Giovanni Visonà, and Niko Beerenwinkel.
⚙️ Install the dependencies
Clone the repository, unzip OriginalData.zip, and install the necessary dependencies ...
A workflow for performing alignment and phylogeny using protein sequences when studying genes/gene families.
MAGNETO
MAGNETO is an automated snakemake workflow dedicated to MAG (Metagenome-Assembled Genomes) reconstruction from metagenomic data.
It includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery.
Key Features
- Quality Control (QC): Automatically assesses the quality and the contamination of input reads, ensuring that low-quality data are filtered out to improve ...
Type: Snakemake
Creators: Samuel Chaffron, Audrey Bihouee, Benjamin Churcheward, Maxime Millet, Guillaume Fertin, Hugo Lefeuvre
Submitter: Hugo Lefeuvre
RNA-SeqEZPZ is a pipeline to run analysis of RNA-Seq experiments from raw FASTQ files all the way to differential genes analysis. The pipeline is accessible through a graphical user interface implemented using a Shiny app and features interactive plots. Advanced users have the ability to customize the scripts provided with the pipeline. This pipeline is designed to run on an HPC cluster. Please cite [1] if you are using this pipeline for a publication.
Installation
In order to use the ...
RNA-SeqEZPZ-NF
Nextflow Pipeline for RNA-SeqEZPZ
A Point-and-Click Pipeline for Comprehensive Transcriptomics Analysis with Interactive Visualizations
RNA-SeqEZPZ-NF is another implementation of RNA-SeqEZPZ. RNA-SeqEZPZ-NF uses the same user interface as RNA-SeqEZPZ and runs the same pipeline, but runs the pipeline implemented by Nextflow. This pipeline is currently tested on HPC cluster with SLURM scheduler. Advanced ...
Based on the Tail analysis workflow at https://git.embl.org/grp-cba/tail-analysis/-/blob/main/analysis_workflow.md
DUNE: Deep feature extraction by UNet-based Neuroimaging-oriented autoEncoder
A versatile neuroimaging encoder that captures brain complexity across multiple diseases: cancer, dementia and schizophrenia.
Overview
DUNE (Deep feature extraction by UNet-based Neuroimaging-oriented autoEncoder) is a neuroimaging-oriented deep learning model designed to extract deep features from multisequence brain MRIs, enabling their processing by basic machine learning algorithms. This project provides an ...
Description
The Settlement Delineation and Analysis (SDA) workflows generates a settlement network from geospatial settlement data. It can process geotiff and shapefile inputs and was originally designed to operate on the World Settlement Footprint dataset. Through multiple workflow stages, a settlement network is constructed, contracted (i.e. clustered) and ultimately analysed with centrality measures. The output shapefile stores the ...
Colorectal-cancer-detection-using-ColoPola-dataset
Methods
We trained and tested three models from scratch (CNN, CNN_2 and EfficientFormerV2) and two pretrained models (DenseNet121 and EfficientNetV2-m) to classify the colorectal cancer using the ColoPola dataset.
ColoPola dataset
The dataset consists of 572 slices (specimens) with 20,592 images. There are 284 cancer samples and 288 normal samples. This dataset can download from Zenodo repository. ...
Type: Python
Creators: Thi-Thu-Hien Pham, Thao-Vi Nguyen, The-Hiep Nguyen, Quoc-Hung Phan, Thanh-Hai Le
Submitter: Thanh-Hai Le
CryoDataBot
CryoDataBot is an automated pipeline designed to streamline dataset generation for cryogenic electron microscopy (cryoEM)-based atomic model building. It supports large-scale AI training and benchmarking by providing standardized tools for data retrieval, preprocessing, labeling, and quality control. CryoDataBot enables flexible configurations for diverse biomolecular structures, improves modeling reproducibility, and facilitates retraining of AI models such as U-Net and CryoREAD. ...
Type: Python
Creators: Qibo Xu, Hong Zhou, Leon Wu, Micahel Rebelo, Shi Feng, Star Yu, Farhanaz Farheen, Daisuke Kihara
Submitter: Qibo Xu