基于非线性网络的转录组数量性状预测
Nonlinear network-based quantitative trait prediction from transcriptomic data
摘要：
通过一组候选基因的表达变化来定量预测表型变量在分子生物学中具有很大的意义，但由于几个原因，它也是一项具有挑战性的任务。首先，收集的生物学观察结果可能是异质的，对应于不同的生物学机制。其次，用于预测表型的基因表达变量可能高度相关，因为基因通过未知的调控网络相互作用。在本文中，我们提出了一种新的方法，旨在预测数量性状的转录组数据，考虑到生物样品的异质性和隐藏的基因调控网络的不同的生物机制。所提出的模型在预测上表现良好，但它也是完全参数化的，这有利于下游生物学解释。该模型根据基因表达数据和表型之间的关系提供个体聚类，并且还导致推断针对每个个体聚类的基因调控网络。我们进行数值模拟，以证明我们的模型与其他预测模型是有竞争力的，我们证明了我们的模型的预测性能和可解释性，从果蝇遗传参考小组（DGRP）的真实数据的转录组数据预测酒精敏感性。
英文摘要：
Quantitatively predicting phenotype variables by the expression changes in a set of candidate genes is of great interest in molecular biology but it is also a challenging task for several reasons. First, the collected biological observations might be heterogeneous and correspond to different biological mechanisms. Secondly, the gene expression variables used to predict the phenotype are potentially highly correlated since genes interact though unknown regulatory networks. In this paper, we present a novel approach designed to predict quantitative trait from transcriptomic data, taking into account the heterogeneity in biological samples and the hidden gene regulatory networks underlying different biological mechanisms. The proposed model performs well on prediction but it is also fully parametric, which facilitates the downstream biological interpretation. The model provides clusters of individuals based on the relation between gene expression data and the phenotype, and also leads to infer a gene regulatory network specific for each cluster of individuals. We perform numerical simulations to demonstrate that our model is competitive with other prediction models, and we demonstrate the predictive performance and the interpretability of our model to predict alcohol sensitivity from transcriptomic data on real data from Drosophila Melanogaster Genetic Reference Panel (DGRP).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
