解码纵向微生物组轨迹:一种可解释的机器学习方法,用于生物标志物的发现和预测
Yifan Dai1, Yunzhi Qian2, Yixiang Qu1
1Department of Biostatistics, Gillings School of Global Public Health at University of North Carolina at Chapel Hill, NC, United States.
Briefings in bioinformatics
|August 12, 2025
概括
我们开发了LP-Micro,这是一种用于分析纵向微生物群数据的机器学习框架. 它准确地预测疾病的结果,并随着时间的推移确定关键的微生物生物标志物.
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.
- 机器学习在卫生中的应用.
背景情况:
- 纵向微生物组数据为疾病发展和进展提供了洞察力.
- 动态微生物数据的预测建模存在分析挑战.
- 识别时间变化的微生物生物标志物对于早期诊断和干预至关重要.
研究的目的:
- 引入LP-Micro,这是一个强大的和可解释的机器学习框架,用于纵向微生物组分析.
- 通过时间变化的微生物效应,增强对病原遗传机制的理解.
- 使用微生物组数据,提高对疾病结果的预测准确性.
主要方法:
- 长度微生物特征选使用多项式组激光.
- 使用机器学习模型 (XGBoost,深度神经网络) 预测疾病结果.
- 通过换进行可解释的关联测试具有重要意义.
主要成果:
- 在模拟中,LP-Micro准确地识别了与疾病相关的微生物群.
- 该框架显示了比现有方法更好的预测准确性.
- 在儿童牙科疾病和腹腔外科手术后减肥的应用显示出高预测准确度.
结论:
- LP-Micro有效地分析了纵向微生物组数据,用于预测建模.
- 该框架强调了关键时间点和与疾病结果相关的微生物变化.
- 这些发现符合临床预期,并提前应用微生物组研究.
相关概念视频
Longitudinal Studies
247
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
247
Longitudinal Research
12.5K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.5K
Introduction To Survival Analysis
397
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
397
Modern Molecular Taxonomy
141
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
141


