A deep learning approach to identify association of disease–gene using information of disease symptoms and protein sequences,Analytical Methods

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A deep learning approach to identify association of disease–gene using information of disease symptoms and protein sequences
Analytical Methods ( IF 2.7 ) Pub Date : 2020-03-11 , DOI: 10.1039/c9ay02333j
Xingyu Chen _{1,

2,

3,

4} , Qixing Huang _{1,

2,

3,

4} , Yang Wang _{3,

4,

5,

6} , Jinlong Li _{1,

2,

3,

4} , Haiyan Liu _{1,

2,

3,

4} , Yun Xie _{1,

2,

3,

4} , Zong Dai _{3,

4,

5,

6} , Xiaoyong Zou _{3,

4,

5,

6} , Zhanchao Li _{1,

2,

3,

4,

7}

Affiliation

Identifying the association of disease–gene is one of the significant steps in understanding pathogenesis and discovering therapeutic targets. Symptoms of disease and sequences of protein are important resources for recognizing the relationship between disease and gene. This study provides a new method for identifying disease-associated genes. In the meantime, symptomatic information and primary structural features are utilized to characterize disease and protein, respectively. A grayscale image is adopted to represent disease–gene association. A convolutional neural network is employed to construct a model for identifying potential disease-associated genes. The accuracy and sensitivity of the training set are 92.38% and 91.17%, respectively, and those of the test set are 80.64% and 80.69%, respectively. Furthermore, predicted potential genes are supported by access to the literature and databases as well as enrichment analysis, demonstrating that the current method can be effectively used for the prediction of disease genes. The source code of Matlab is freely available on request to the authors.

中文翻译：

使用疾病症状和蛋白质序列信息识别疾病与基因关联的深度学习方法

识别疾病与基因的关联是了解发病机理和发现治疗靶标的重要步骤之一。疾病的症状和蛋白质序列是认识疾病与基因之间关系的重要资源。这项研究提供了一种鉴定疾病相关基因的新方法。同时，症状信息和主要结构特征分别用于表征疾病和蛋白质。采用灰度图像表示疾病与基因的关联。卷积神经网络用于构建用于识别潜在疾病相关基因的模型。训练集的准确性和敏感性分别为92.38％和91.17％，而测试集的准确性和敏感性分别为80.64％和80.69％。此外，可以通过访问文献和数据库以及进行富集分析来支持可预测的潜在基因，这表明当前的方法可以有效地用于疾病基因的预测。Matlab的源代码可应要求免费提供给作者。

更新日期：2020-04-24

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