纳入代谢活动,分类学和社区结构,以改善基于微生物组的预测模型,用于宿主表型预测
Mahsa Monshizadeh1, Yuzhen Ye1
1Computer Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.
Gut microbes
|January 12, 2024
概括
我们创建了MicroKPNN,这是一个新的可解释的神经网络,它使用先前的知识来从肠道微生物组数据中预测人类宿主表型. 这种方法提高了预测准确度,并为各种疾病提供了对微生物组与宿主相互作用的见解.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 主体微生物群的相互作用
背景情况:
- 人的肠道微生物组在宿主健康和疾病中起着至关重要的作用.
- 从微生物组数据中预测宿主表型是具有挑战性的,因为复杂性.
- 现有的计算方法往往缺乏可解释性.
研究的目的:
- 开发一种新的可解释的神经网络,MicroKPNN,用于基于微生物组的人类宿主表型预测.
- 将先前的生物知识整合到机器学习模型中,以提高准确性.
- 为微生物组和宿主表型之间的关系提供可解释的见解.
主要方法:
- 开发了MicroKPNN,这是一个以先前知识为导向的浅层神经网络.
- 纳入细菌代谢活动,基因关系和社区结构作为事先的知识.
- 应用MicroKPNN对五种人类疾病的七个肠道微生物群数据集.
主要成果:
- 与所有数据集中完全连接的神经网络相比,MicroKPNN显著提高了宿主表型预测的准确性.
- 在所有测试的情况下,MicroKPNN的表现都超过了DeepMicro的深度学习方法.
- 该模型为隐藏节点提供了可解释的重要性得分,解释了微生物组对预测的贡献.
结论:
- 之前的知识整合提高了基于微生物组的表型预测准确度.
- MicroKPNN为研究宿主微生物群相互作用提供了一个强大而易于解释的工具.
- 该方法可以验证现有发现,并建议微生物组科学领域的新研究途径.
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