Li Genyuan, Lee Olivia, Rabitz Herschel
Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States of America.
Peddie School, Hightstown, New Jersey 08520, United States of America.
PLoS One. 2018 Feb 15;13(2):e0192867. doi: 10.1371/journal.pone.0192867. eCollection 2018.
Autism spectrum disorder (ASD) is a wide-ranging collection of developmental diseases with varying symptoms and degrees of disability. Currently, ASD is diagnosed mainly with psychometric tools, often unable to provide an early and reliable diagnosis. Recently, biochemical methods are being explored as a means to meet the latter need. For example, an increased predisposition to ASD has been associated with abnormalities of metabolites in folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS). Multiple metabolites in the FOCM/TS pathways have been measured, and statistical analysis tools employed to identify certain metabolites that are closely related to ASD. The prime difficulty in such biochemical studies comes from (i) inefficient determination of which metabolites are most important and (ii) understanding how these metabolites are collectively related to ASD. This paper presents a new method based on scores produced in Support Vector Machine (SVM) modeling combined with High Dimensional Model Representation (HDMR) sensitivity analysis. The new method effectively and efficiently identifies the key causative metabolites in FOCM/TS pathways, ranks their importance, and discovers their independent and correlative action patterns upon ASD. Such information is valuable not only for providing a foundation for a pathological interpretation but also for potentially providing an early, reliable diagnosis ideally leading to a subsequent comprehensive treatment of ASD. With only tens of SVM model runs, the new method can identify the combinations of the most important metabolites in the FOCM/TS pathways that lead to ASD. Previous efforts to find these metabolites required hundreds of thousands of model runs with the same data.
自闭症谱系障碍（ASD）是一系列具有不同症状和残疾程度的发育性疾病。目前，ASD主要通过心理测量工具进行诊断，这些工具往往无法提供早期且可靠的诊断。最近，人们正在探索生化方法以满足后一种需求。例如，ASD易感性增加与叶酸依赖性一碳代谢（FOCM）和转硫途径（TS）中的代谢物异常有关。已经测量了FOCM/TS途径中的多种代谢物，并使用统计分析工具来识别与ASD密切相关的某些代谢物。此类生化研究的主要困难在于：（i）难以有效确定哪些代谢物最为重要；（ii）难以理解这些代谢物与ASD之间的整体关系。本文提出了一种基于支持向量机（SVM）建模产生的分数并结合高维模型表示（HDMR）敏感性分析的新方法。该新方法有效且高效地识别FOCM/TS途径中的关键致病代谢物，对其重要性进行排序，并发现它们对ASD的独立和相关作用模式。这些信息不仅对于提供病理解释的基础很有价值，而且对于潜在地提供早期、可靠的诊断并最终实现对ASD的全面治疗也很有价值。仅通过运行数十次SVM模型，新方法就能识别出FOCM/TS途径中导致ASD的最重要代谢物的组合。以前使用相同数据寻找这些代谢物的努力需要运行数十万次模型。
