GenePioneer: A Comprehensive Python Package for Identification of Essential Genes and Modules in Cancer

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Summary

We propose a network-based unsupervised learning model to identify essential cancer genes and modules for 12 different cancer types, supported by a Python package for practical application. The model constructs a gene network from frequently mutated genes and biological processes, ranks genes using topological features, and detects critical modules. Evaluation across cancer types confirms its effectiveness in prioritizing cancer-related genes and uncovering relevant modules. The Python package allows users to input gene lists, retrieve rankings, and identify associated modules. This work providing a robust method for gene prioritization and module detection, along with a user-friendly package to support research and clinical decision-making in cancer genomics.

Availability

GenePioneer is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/Golnazthr/ModuleDetection

Contact

golnazt@kth.se and haerian.amirhossein@gmail.com

Article activity feed