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分类依据与纠错

AI 根据所提供的论文材料归类:摘要主要研究生物学潜变量与多元二元响应模型,虽讨论因果假说,但未明确提供因果效应识别方法。

反馈分类问题

Application of Multidimensional Selective Item Response Regression Model for Studying Multiple Gene Methylation in SV40 Oncogenic Pathways

用多维选择性项目反应回归研究 SV40 致癌通路中的多基因甲基化

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Haiqun Lin
Ziding Feng
Yan Yu
Yingye Zheng
Adi F Gazdar

用多维选择性项目反应回归研究 SV40 致癌通路中的多基因甲基化

研究问题
如何用多基因甲基化的二元数据描述风险因素、潜在分子通路与癌症状态的关系,并检验数据是否符合特定因果假说?
方法与思路
作者构建多维潜因子的选择性项目反应回归,以最大似然估计参数,并用遗传算法搜索甲基化响应与潜因子间的连接。
材料陈述的贡献
摘要称模型允许风险因素改变潜因子分布,提出模型内的经验识别条件,并以 SV40、甲基化和癌症状态数据检验通路假说。
阅读关注点
应核对潜因子的识别条件、连接搜索与参数估计如何配合,以及因果假说的可检验含义和机制解释的额外依据。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明,不能推断刊本变化;与因果假说一致不等于识别因果效应,具体识别条件未说明。

原文摘要

Alteration of gene methylation patterns has been reported to be involved in the early onsets of many human malignancies. Many exogenous risk factors, such as cigarette smoke, dietary additives, chemical exposures, radiation, and biologic agents including viral infection, are involved in the methylation pathways of cancers. We propose a multidimensional selective item response regression model to describe and test how a risk factor may alter molecular pathways involving aberrant methylation of multiple genes in oncogenesis. Our modeling framework is built on an item response model for multivariate dichotomous responses of high dimension, such as aberrant methylation of multiple tumor-suppressor genes, but we allow risk factors such as SV40 viral infection to alter the distribution of the latent factors that subsequently affect the outcome of cancer. We postulate empirical identification conditions under our model formulation. Moreover, we do not prespecify the links between the multiple dichotomous methylation responses and the latent factors, but rather conduct specification searches with a genetic algorithm to discover the links. Parameter estimation through maximum likelihood and specification searches in models with multidimensional latent factors for multivariate binary responses have become practical only recently, due to modern statistical computing development. We illustrate our proposal with the biological finding that simultaneous methylation of multiple tumor-suppressor genes is associated with the presence of SV40 viral sequences and with the cancer status of lymphoma/leukemia.We are able to test whether the data are consistent with the causal hypothesis that SV40 induces aberrant methylation of multiple genes in its oncogenic pathways. At the same time, we are able to evaluate the role of SV40 in the methylation pathway and to determine whether the methylation pathway is responsible for the development of leukemia/lymphoma.

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