A Gene-disease Association Prediction Algorithm Based on Multi-source Data Fusion

Q4 Agricultural and Biological Sciences International Journal Bioautomation Pub Date : 2022-03-01 DOI:10.7546/ijba.2022.26.1.000870
Fei Wang
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Abstract

Accurate gene-disease association prediction results are the basis for effective diagnosis and treatment of complex genetic diseases. However, existing studies related to this topic generally face problems in two aspects: large volume of original data and diverse data type, and data fusion difficulty. Therefore, this paper studied a gene-disease association prediction algorithm based on multi-source data fusion. At first, it processed the multi-dimensional gene phenotype data, analyzed the gene-disease associations of different phenotypes, and completed the selection of disease gene loci under multi-dimensional phenotypes. Then, this paper fused the multi-source data containing the gene expression data, gene sequence data, gene interaction data, and transcriptome sequencing data, and established the corresponding gene-disease association prediction model. At last, the effectiveness of the constructed prediction model was verified by experimental results. The research results obtained in this paper can improve the low utilization of gene datasets, restored the main features of the datasets to the greatest extent, reasonably processed the data noise, effectively enhanced the robustness of the model, and further improved the classification accuracy of the prediction of disease-causing genes.
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一种基于多源数据融合的基因疾病关联预测算法
准确的基因-疾病关联预测结果是有效诊断和治疗复杂遗传疾病的基础。然而,现有与该主题相关的研究普遍面临两个方面的问题:原始数据量大、数据类型多样,以及数据融合困难。因此,本文研究了一种基于多源数据融合的基因疾病关联预测算法。首先,它处理了多维基因表型数据,分析了不同表型的基因-疾病关联,完成了多维表型下疾病基因座的选择。然后,本文融合了包含基因表达数据、基因序列数据、基因相互作用数据和转录组测序数据的多源数据,建立了相应的基因-疾病关联预测模型。最后,通过实验验证了所构建的预测模型的有效性。本文获得的研究结果可以改善基因数据集利用率低的问题,最大限度地恢复数据集的主要特征,合理处理数据噪声,有效增强模型的稳健性,进一步提高致病基因预测的分类精度。
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来源期刊
International Journal Bioautomation
International Journal Bioautomation Agricultural and Biological Sciences-Food Science
CiteScore
1.10
自引率
0.00%
发文量
22
审稿时长
12 weeks
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