我中心研究员林存洁在《Biometrics》发表论文
2021-02-09
我中心研究员林存洁在《Biometrics》发表论文。该研究主要关注基因表达数据的网络结构分析问题，提出一种加权先验信息的方法，可以有效融合已有研究成果的信息，进而改善当前数据下高斯图模型的估计结果。在该研究中探讨了估计方法的理论性质，并通过模拟和实际数据分析验证了该方法的优越性。
论文题目
Information‐incorporated Gaussian graphical model for gene expression data
作者介绍
林存洁，中国人民大学统计学院副教授、应用统计科学研究中心研究员，主要从事缺失数据分析、模型选择、模型平均、生存分析、分位数回归等领域研究，研究论文发表于Statistica Sinica, Statistics in Medicine、Journal of Multivariate Analysis、统计研究等国内外权威杂志。
英文摘要
In the analysis of gene expression data, network approaches take a system perspective and have played an irreplaceably important role. Gaussian graphical models (GGM) have been popular in the network analysis of gene expression data. They investigate the conditional dependence between genes and “transform” the problem of estimating network structures into a sparse estimation of precision matrices. When there is a moderate to large number of genes, the number of parameters to be estimated may overwhelm the limited sample size, leading to unreliable estimation and selection. In this article, we propose incorporating information from previous studies (for example, those deposited at PubMed) to assist estimating the network structure in the present data. It is recognized that such information can be partial, biased, or even wrong. A penalization-based estimation approach is developed, shown to have consistency properties, and realized using an effective computational algorithm. Simulation demonstrates its competitive performance under various information accuracy scenarios. The analysis of TCGA lung cancer prognostic genes leads to network structures different from the alternatives.
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