基于多源特征和深度学习,预测潜在的微生物疾病关联
Liugen Wang1, Yan Wang2, Chenxu Xuan2
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Briefings in bioinformatics
|July 5, 2023
概括
这项研究介绍了DSAE_RF,一种计算模型,可以有效地预测微生物与疾病的关联. 它利用深度学习来减少识别与疾病相关的微生物的时间和成本,帮助临床研究.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 复杂的疾病与微生物群落有关,影响瘤发生和转移.
- 临床观察疾病中的微生物群存在重大差距.
- 目前用于识别与疾病相关的微生物的生物实验是准确的,但耗时且昂贵.
研究的目的:
- 开发一种计算模型,有效预测微生物与疾病的关联.
- 为了降低与识别与疾病相关的微生物相关的资本和时间成本.
- 解决微生物群研究中传统实验方法的局限性.
主要方法:
- 开发了一个新型模型,DSAE_RF,它结合了多源功能和深度学习.
- 计算了微生物和疾病之间的四个相似之处,以创建特征向量.
- 采用k-means集群用于可靠的负样本选,以及用于特征提取的深度稀疏自编码器.
- 利用随机森林分类器来预测微生物与疾病的关联.
主要成果:
- DSAE_RF模型实现了高性能,其AUC为0.9448和AUPR为0.9431.
- 经过10倍交叉验证的广泛验证证明了模型的可靠性.
- 包括负样本选择方法,模型比较和案例研究 (Covid-19,结直肠癌) 在内的比较分析证实了该模型的有效性.
结论:
- DSAE_RF模型提供了一种可靠和高效的计算方法,用于预测微生物与疾病的关联.
- 这种方法大大降低了识别与疾病相关的微生物所需的成本和时间.
- 这些发现支持计算模型在推进临床微生物群研究和疾病理解方面的潜力.
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