Lin Yenching, Yerukala Sathipati Srinivasulu, Ho Shinn-Ying
Interdisciplinary Neuroscience Ph.D. Program, National Chiao Tung University, Hsinchu, Taiwan.
Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, United States.
Front Genet. 2021 Jun 14;12:665469. doi: 10.3389/fgene.2021.665469. eCollection 2021.
Autism spectrum disorder (ASD) refers to a wide spectrum of neurodevelopmental disorders that emerge during infancy and continue throughout a lifespan. Although substantial efforts have been made to develop therapeutic approaches, core symptoms persist lifelong in ASD patients. Identifying the brain temporospatial regions where the risk genes are expressed in ASD patients may help to improve the therapeutic strategies. Accordingly, this work aims to predict the risk genes of ASD and identify the temporospatial regions of the brain structures at different developmental time points for exploring the specificity of ASD gene expression in the brain that would help in possible ASD detection in the future. A dataset consisting of 13 developmental stages ranging from 8 weeks post-conception to 8 years from 26 brain structures was retrieved from the BrainSpan atlas. This work proposes a support vector machine-based risk gene prediction method ASD-Risk to distinguish the risk genes of ASD and non-ASD genes. ASD-Risk used an optimal feature selection algorithm called inheritable bi-objective combinatorial genetic algorithm to identify the brain temporospatial regions for prediction of the risk genes of ASD. ASD-Risk achieved a 10-fold cross-validation accuracy, sensitivity, specificity, area under a receiver operating characteristic curve, and a test accuracy of 81.83%, 0.84, 0.79, 0.84, and 72.27%, respectively. We prioritized the temporospatial features according to their contribution to the prediction accuracy. The top identified temporospatial regions of the brain for risk gene prediction included the posteroventral parietal cortex at 13 post-conception weeks feature. The identified temporospatial features would help to explore the risk genes that are specifically expressed in different brain regions of ASD patients.
自闭症谱系障碍（ASD）是指在婴儿期出现并持续一生的一系列广泛的神经发育障碍。尽管已经做出了大量努力来开发治疗方法，但ASD患者的核心症状会终身存在。识别ASD患者中风险基因表达的脑时空区域可能有助于改进治疗策略。因此，这项工作旨在预测ASD的风险基因，并识别不同发育时间点脑结构的时空区域，以探索ASD基因在脑中表达的特异性，这将有助于未来可能的ASD检测。从BrainSpan图谱中检索了一个数据集，该数据集由26个脑结构从受孕后8周到8岁的13个发育阶段组成。这项工作提出了一种基于支持向量机的风险基因预测方法ASD-Risk，以区分ASD的风险基因和非ASD基因。ASD-Risk使用了一种名为可遗传双目标组合遗传算法的最优特征选择算法来识别用于预测ASD风险基因的脑时空区域。ASD-Risk在10倍交叉验证中的准确率、灵敏度、特异性、受试者工作特征曲线下面积和测试准确率分别为81.83%、0.84、0.79、0.84和72.27%。我们根据时空特征对预测准确率的贡献对其进行了排序。在风险基因预测中确定的脑的顶级时空区域包括受孕后13周特征时的后腹侧顶叶皮层。确定的时空特征将有助于探索在ASD患者不同脑区中特异性表达的风险基因。
