Chen Lei, Chu Chen, Zhang Yu-Hang, Zhu Changming, Kong Xiangyin, Huang Tao, Cai Yu-Dong
School of Life Sciences, Shanghai University, Shanghai, 200444, China.
College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China.
PLoS One. 2016 Jul 19;11(7):e0159395. doi: 10.1371/journal.pone.0159395. eCollection 2016.
The human brain is one of the most mysterious tissues in the body. Our knowledge of the human brain is limited due to the complexity of its structure and the microscopic nature of connections between brain regions and other tissues in the body. In this study, we analyzed the gene expression profiles of three brain regions-the brain stem, cerebellum and cerebral cortex-to identify genes that are differentially expressed among these different brain regions in humans and to obtain a list of robust, region-specific, differentially expressed genes by comparing the expression signatures from different individuals. Feature selection methods, specifically minimum redundancy maximum relevance and incremental feature selection, were employed to analyze the gene expression profiles. Sequential minimal optimization, a machine-learning algorithm, was employed to examine the utility of selected genes. We also performed a literature search, and we discuss the experimental evidence for the important physiological functions of several highly ranked genes, including NR2E1, DAO, and LRRC7, and we give our analyses on a gene (TFAP2B) that have not been investigated or experimentally validated. As a whole, the results of our study will improve our ability to predict and understand genes related to brain regionalization and function.
人类大脑是人体中最神秘的组织之一。由于其结构的复杂性以及大脑区域与身体其他组织之间连接的微观性质,我们对人类大脑的了解有限。在本研究中,我们分析了三个脑区——脑干、小脑和大脑皮层——的基因表达谱,以鉴定在人类这些不同脑区中差异表达的基因,并通过比较不同个体的表达特征来获得一组可靠的、区域特异性的差异表达基因列表。采用特征选择方法,特别是最小冗余最大相关法和增量特征选择法来分析基因表达谱。使用一种机器学习算法——序列最小优化算法来检验所选基因的效用。我们还进行了文献检索,并讨论了几个排名靠前的基因(包括NR2E1、DAO和LRRC7)重要生理功能的实验证据,并且我们对一个尚未被研究或实验验证的基因(TFAP2B)进行了分析。总体而言,我们的研究结果将提高我们预测和理解与脑区域化和功能相关基因的能力。