预测基因疾病的关联与知识图的嵌入,以减少信息的疾病
Francesco Gualdi1,2, Baldomero Oliva2, Janet Piñero1,3
1Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (IBI-GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain.
NAR genomics and bioinformatics
|May 15, 2024
概括
知识图嵌入 (KGE) 有效地优先考虑与疾病相关的基因. 新型算法Dlemb和BioKG2vec在预测诸如椎间盘退化 (IDD) 等复杂疾病的基因方面表现出卓越的表现.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
背景情况:
- 知识图嵌入 (KGE) 代表了低维空间中的复杂生物知识.
- 了解KGE在具有有限遗传数据的复杂疾病中优先考虑基因的应用是至关重要的.
研究的目的:
- 开发和评估新的KGE算法,以优先考虑与疾病相关的基因.
- 评估数据质量和整合对预测准确性的影响.
- 应用KGE来识别与椎间盘退化 (IDD) 相关的基因.
主要方法:
- 构建了一个生物医学知识图 (KG),整合了异质数据.
- 使用最先进的方法和两个新的算法生成KGE:Dlemb和BioKG2vec.
- 使用无监督集群和监督预测方法验证的嵌入.
主要成果:
- KGE成功预测了与复杂疾病相关的基因.
- 新的Dlemb和BioKG2vec算法在基因优先排序方面表现优于现有的方法.
- 数据质量,预处理和集成显著影响预测准确性.
- 针对IDD的优先基因显示了疾病相关功能的丰富.
结论:
- 在生物医学研究中,KGE是对基因优先级的宝贵工具.
- 开发的Dlemb和BioKG2vec算法为疾病基因发现提供了更好的性能.
- 对数据质量和整合的重视对于基因组学中强大的KGE应用至关重要.
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