| 周 莉,常 菁,郭 珣,张 艺,王志丹.基于PCA和神经网络的多形性胶质母细胞瘤驱动基因预测模型[J].,2017,17(33):6553-6556 |
| 基于PCA和神经网络的多形性胶质母细胞瘤驱动基因预测模型 |
| Identification of Cancer Driver Gene Model for Glioblastoma Multiforme Based on PCA and Neural Network Approaches |
| 投稿时间：2017-03-28 修订日期：2017-04-23 |
| DOI：10.13241/j.cnki.pmb.2017.33.035 |
| 中文关键词: 驱动基因 基因表达谱 神经网络 系统生物学 主成分分析 |
| 英文关键词: Driver genes Expression profile Neural network Systematic biology PCA |
| 基金项目: |
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| 中文摘要: |
| 摘要 目的：通过对癌症基因表达数据的分析，预测多形性胶质母细胞瘤的驱动基因集。方法：基于主成分分析方法和神经网络，提出一种用于预测多形性胶质母细胞瘤驱动基因的系统生物学模型。首先对实验样本的原始表达谱数据进行预清洗，过滤掉无信息或表达不符合实验要求的表达数据，并对肿瘤表达谱数据进行标准化处理；然后对基因进行划分，相似突变率的基因将被划分到同一块中；最后通过学习神经网络，构建癌症相关基因的调控网络，得出驱动基因的预测集。结果：本研究应用上述模型，对多形性胶质母细胞瘤(glioblastoma multiforme, GBM)驱动基因进行预测。已发表的大量实验结果表明，我们预测出的大部分驱动基因在GBM中起重要作用。结论：我们提出一种对GBM表达谱数据分析的新方法，能够高精度地预测出该疾病的驱动基因，该模型同样能够较好地用于分析其它疾病的表达谱数据。 |
| 英文摘要: |
| ABSTRACT Objective: Predicting a set of cancer driver genes by analyzing gene expression data of glioblastoma multiforme. Methods: We proposed a systematic approach to predicting driver genes for glioblastoma multiforme based on Principal Component Analysis and training neural networks. First, the rawgene expression data were processed to filter out non-informative and low-expression data and then normalizing the tumor expression data . Second, we grouped the genes so that the ones with similar expression fold changes belong to the same group. Finally, in order to predict the driver gene set, we reconstruct cancer-related genes regulatory network through Neural network learning. Results: In our study, we predicted a set of driver genes for glioblastoma multiforme, most of which played an impor- tant role in the carcinogenic process, as demonstrated by existing literatures. Conclusion: We proposed a new, general neural network model to analyze glioblastoma multiforme expression data, which is able to predict the set of drive genes for this disease with high accu- racy. The proposed approach could also be used to analyze the gene expression data of other important diseases. |
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