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>An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs
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An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs
We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22–36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index 3 kg m ?2 ). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.
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机译：我们组合临床，细胞因子，基因组，甲基化和膳食数据来自43名年轻成人单吞咽双对（年龄22-36岁，女性），其中25个双对基本重量不协调（Delta体重指数> 3kg m ？2）。这些测量最初是作为Twinfat研究的一部分，是芬兰双胞胎队队的研究。使用称为群体因子分析（GFA）的一体化机器学习方法共同分析这五个大的多变量数据集（分别包括42,71,1587,1605和63变量），以提供新的假设进入与之相关的多分子水平相互作用肥胖的发展。鉴定细胞因子和体重增加之间的新势联系，以及膳食，炎症和表观因素之间的关联。这项令人鼓舞的案例研究旨在激励研究界大胆地尝试新的机器学习方法，这些方法有可能产生新颖和无需假设。 GFA方法的源代码作为R包GFA公开可用。
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