DEEPrior：一种用于基因融合优先级排序的深度学习工具。
DEEPrior: a deep learning tool for the prioritization of gene fusions.
机构信息
Department of Control and Computer Engineering.
Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Torino 10129, Italy.
出版信息
Bioinformatics. 2020 May 1;36(10):3248-3250. doi: 10.1093/bioinformatics/btaa069.
SUMMARY
In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user.
AVAILABILITY AND IMPLEMENTATION
Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
在过去的十年中，人们越来越关注基因融合的研究。然而，确定基因融合是致癌驱动因素还是仅仅是乘客突变的问题仍然没有得到解决。在这里，我们提出了 DEEPrior，这是一种具有两种模式（推断和重新训练）的固有灵活的深度学习工具。推断模式通过直接利用融合蛋白的氨基酸序列来预测基因融合是否参与致癌过程的概率。重新训练模式允许获得包括用户提供的新数据的自定义预测模型。
可用性和实现
DEEPrior 和蛋白质融合数据集均可从 GitHub（https://github.com/bioinformatics-polito/DEEPrior）免费获得。该工具旨在在 Python 3.7 中运行，仅需要最少的附加库。
补充信息
补充数据可在 Bioinformatics 在线获得。
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