CPSM: Cancer patient survival model - Workflow

Introduction

CPSM is an R package that provides a comprehensive computational pipeline for predicting survival probabilities and risk groups in cancer patients. It includes dedicated modules to perform key steps such as data preprocessing, training/test splitting, and normalization. CPSM enables feature selection through univariate cox-regression survival analysis, feature selection though LASSO method, and calculates a LASSO-based Prognostic ...

Integrative Prediction Strategy (IPS) predicts brain gene expression from blood-derived features using pre-trained machine learning models. The workflow integrates multiple feature selection strategies (unsupervised and supervised) and evaluates model performance across cross-validation folds.

Workflow Steps:

1. Load matched blood and brain gene expression data.
2. Apply feature selection:

• Unsupervised feature selection (features selected without using the target brain gene)
• Supervised ...

Workflow Overview

This Galaxy workflow allows transferring ARC imaging data stored as .ZIP file to a target OMERO instance by 1) keeping the ARC structure and 2) automatize the metadata annotation of the assay.

How does it works

The workflow extract all the ./assays/experiment/dataset/ file path and create an OMERO dataset containing the images. The "isa.assay.xlsx" file is used to add key-value pairs to the dataset.

Workflow Inputs

The workflow as three different data inputs: ...

RNASeqCompTest

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This is the source code for an applet that runs on the DNAnexus Platform. This applet calculates the total storage for each user in a given folder in a given project on the UK Biobank Research Analysis Platform (UKB-RAP).

Applet inputs

The applet has three inputs:

1. project: the name of the UKB-RAP project in which to calculate the data storage.
2. folder: the name of the folder in the UKB-RAP project to be searched (if not a top-level folder then the full path should be given e.g., ...

Quantifying the unknown on MS1 level. UnbeQuant allows the quantification of measured ions without identification annotations in a DDA setting for mass spectrometry proteomics data from Bruker or Thermo mass spectrometers. To achieve this it uses identification results and sets same identifications across runs as anchors to align multiple runs, providing a mixture of the following: Identified ions with quantitative values, only some identified ...

CHAMPAGNE 🍾

CHromAtin iMmuno PrecipitAtion sequencinG aNalysis pipEline

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The Human–AI Ledger (HAIL) defines a structured, repeatable workflow for human–AI collaboration. Through standardized checkpoints and a session ledger, HAIL documents ethical, creative, and procedural context across both human and AI contributions. While AI-generated outputs are inherently non-deterministic, HAIL supports process reproducibility by providing a consistent framework for recording collaboration, facilitating auditability, transparency, and ethical accountability in co-creative AI ...

Summary

This notebook shows how to integrate genomic and image data resources. This notebook looks at the question Which diabetes related genes are expressed in the pancreas?

Steps:

• Query humanmine.org, an integrated database of Homo sapiens genomic data using the intermine API to find the genes.
• Using the list of found genes, search in the Image Data Resource (IDR) for images linked to the genes, tissue and disease.

We use the intermine Python API ...