Research progress on multi-omics integrative analysis methods
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摘要: 复杂疾病往往是由环境因素、遗传因素(多个组学层面)共同作用所致。全面分析不同分子水平的信息对认识疾病的发生发展至关重要。多组学数据整合分析能够提高特征筛选检验效能、改善疾病预测精度。本文从统计学角度出发,对多组学数据整合分析的统计理论方法研究进展做一述评。Abstract: Complex diseases are commonly caused by multi-omics functions including environmental and genetic factors. Thus, it is essential to perform comprehensive analysis taking different levels of molecular information together. Integrative analysis of multi-omics data helps improving statistical power of identifying novel disease associated features and prediction accuracy for risk and prognosis. We summarized statistical methods for integrative analysis of multi-omics data in this paper.
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Key words:
- Multi-omics data /
- Integrative analysis /
- Statistical methods
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