潜在的精神分裂症与疾病相关的基因预测使用基于蛋白质-蛋白质相互作用关键词的metagraph表示 网络:框架开发和验证
Shirui Yu1, Ziyang Wang1, Jiale Nan1
1Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.
JMIR formative research
|November 15, 2023
概括
研究人员使用一种新的机器学习方法确定了潜在的精神分裂症风险基因. 这种方法分析蛋白质-蛋白质相互作用和关键词,以发现复杂的疾病关联,为精神分裂症病理学提供新的见解.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 精神病学遗传学 精神病学遗传学
背景情况:
- 精神分裂症是一种严重的精神障碍,越来越多的研究重点.
- 识别基因疾病的关联有助于理解复杂的疾病,如精神分裂症.
- 这项研究旨在发现精神分裂症的新型治疗点.
研究的目的:
- 通过机器学习识别潜在的精神分裂症风险基因.
- 在蛋白质-蛋白质相互作用 (PPI) -关键词 (PPIK) 网络中提取拓和功能蛋白质特征.
- 提出基于PPIK的元段表示,以了解疾病机制.
主要方法:
- 通过整合蛋白质属性和关键词构建了一个PPIK网络.
- 使用元图表提取网络拓特征并将其转换为向量.
- 训练并优化机器学习模型,包括随机森林 (RF) 和梯度增强.
主要成果:
- 拟议的方法实现了接收器运行特征曲线 (AUC) 下的面积在0.72和0.76.7之间.
- 与基线方法相比,PPIK网络和metagraph方法显著改善了预测性能.
- 确定了前20个潜在的精神分裂症风险基因,包括EYA3,CNTN4,HSPA8,LRRK2和AFP,并得到了文献证据的支持.
结论:
- 在PPIK网络上的metagraph表示有效地识别了潜在的精神分裂症风险基因.
- 这些发现是可靠的,并得到现有文献的支持.
- 这种方法提供了关于精神分裂症病因的宝贵生物学见解.
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