在图像卷积网络中排序类型可以提高基于微生物的机器学习准确度
Oshrit Shtossel1, Haim Isakov1, Sondra Turjeman2
1Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel.
Gut microbes
|June 22, 2023
概括
这项研究介绍了iMic,这是一种新的机器学习方法,可以将微生物组数据转换为图像,以改进疾病生物标志物的发现. iMic提高了复杂微生物数据集的分类准确性和可解释性.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 人的肠道微生物组与众多疾病有关,这使得它成为基于机器学习 (ML) 的生物标志物开发的目标.
- 基于微生物序列的研究面临着ML的挑战,包括数据稀疏性,高维度和非均表示.
- 目前的方法与微生物组数据固有的复杂性作斗争,限制了准确的ML应用.
研究的目的:
- 开发一种用于改善微生物组研究中的机器学习应用的新方法.
- 加强微生物分类学的表征和分析,以更准确地识别疾病生物标志物.
- 创建一个可解释的ML框架,以了解微生物组与疾病的关联.
主要方法:
- 使用图形表示来显示cladogram结构与种群频率一样有信息.
- iMic (图像微生物组) 使用代排序方案将微生物组数据翻译为图像.
- 卷积神经网络 (CNN) 应用于生成的图像,可解释的AI用于解释.
主要成果:
- 与最先进的方法相比,iMic在基于静态微生物组基因序列的ML中表现出更高的精度.
- 该方法有效地结合了来自不同种类的信息,改善了ML的数据表示.
- 可解释的人工智能有助于识别与特定疾病相关的类型.
结论:
- iMic为微生物组数据分析和生物标志物发现提供了一种强大而可解释的方法.
- 将微生物组数据转换为图像显著提高了ML模型的性能.
- iMic框架可以扩展到分析动态微生物群样本,为研究开辟新的途径.
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