FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.
FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.
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FI-Net：利用功能影响预测神经网络识别癌症驱动基因
DOI：
10.3389/fgene.2020.564839
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发表时间：
2020
影响因子：
3.7
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Gu H;Xu X;Qin P;Wang J
驱动基因的突变导致肿瘤的发展，识别驱动基因对于提高癌症研究和精准医疗至关重要。为了克服传统的基于频率的方法无法检测到低重复突变驱动基因的问题，研究人员将重点放在基因突变对功能的影响上，并提出了基于功能的方法。然而，大多数基于函数的方法都是通过非参数方法来估计零模型的分布，这对样本量很敏感。此外，这种方法可能会导致选择不足或过度选择的结果。本研究提出了一种基于功能影响预测神经网络（FI-net）的驱动基因识别方法。以多组学特征作为多变量输入，构建了以人工神经网络为参数的基因功能影响评分估计模型。然后利用层次聚类算法对得到的每个聚类进行背景分布估计和驱动基因识别。我们将FI-net和其他22种最先进的方法应用于癌症基因组图谱项目的31个数据集。根据综合评价标准，FI-net在各种数据集中功能强大，在与Cancer Gene Census和Network of Cancer Genes数据库的重叠比例以及预测一致性方面优于其他方法。此外，结果表明FI-net可以识别已知和潜在的新驱动基因。
Identification of driver genes, whose mutations cause the development of tumors, is crucial for the improvement of cancer research and precision medicine. To overcome the problem that the traditional frequency-based methods cannot detect lowly recurrently mutated driver genes, researchers have focused on the functional impact of gene mutations and proposed the function-based methods. However, most of the function-based methods estimate the distribution of the null model through the non-parametric method, which is sensitive to sample size. Besides, such methods could probably lead to underselection or overselection results. In this study, we proposed a method to identify driver genes by using functional impact prediction neural network (FI-net). An artificial neural network as a parametric model was constructed to estimate the functional impact scores for genes, in which multi-omics features were used as the multivariate inputs. Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. According to the comprehensive evaluation criterion, FI-net was powerful among various datasets and outperformed the other methods in terms of the overlap fraction with Cancer Gene Census and Network of Cancer Genes database, and the consensus in predictions among methods. Furthermore, the results illustrated that FI-net can identify known and potential novel driver genes.
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影响因子：
14.9
作者：
Gonzalez-Perez A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
影响因子：
14.9
作者：
Chung IF;Chen CY;Su SC;Li CY;Wu KJ;Wang HW;Cheng WC
通讯作者：
Cheng WC
影响因子：
3.7
作者：
Cerami E;Demir E;Schultz N;Taylor BS;Sander C
通讯作者：
Sander C
DOI：
10.1038/nrc1299
发表时间：
2004-03
期刊：
Nature reviews. Cancer
影响因子：
--
作者：
通讯作者：
--
影响因子：
12.3
作者：
Bashashati A;Haffari G;Ding J;Ha G;Lui K;Rosner J;Huntsman DG;Caldas C;Aparicio SA;Shah SP
通讯作者：
Shah SP