GenePioneer: a comprehensive Python package for identification of essential genes and modules in cancer.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2025-04-29 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf094

Amirhossein Haerianardakani, Golnaz Taheri

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引用次数: 0

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 provides 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 and implementation: 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.

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GenePioneer：一个全面的Python包，用于识别癌症中的基本基因和模块。

摘要：我们提出了一个基于网络的无监督学习模型，用于识别12种不同癌症类型的基本癌症基因和模块，并在Python包的支持下进行实际应用。该模型从频繁突变的基因和生物过程中构建基因网络，利用拓扑特征对基因进行排序，并检测出关键模块。对癌症类型的评估证实了它在确定癌症相关基因的优先级和发现相关模块方面的有效性。Python包允许用户输入基因列表，检索排名，并识别相关模块。这项工作为基因优先排序和模块检测提供了一个强大的方法，以及一个用户友好的软件包，以支持癌症基因组学的研究和临床决策。可用性和实现：GenePioneer在MIT许可下作为开源软件发布。源代码可在GitHub上获得https://github.com/Golnazthr/ModuleDetection。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量