Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
Independent Researcher, Tel Aviv 69978, Israel.
Int J Mol Sci. 2023 Jan 20;24(3):2082. doi: 10.3390/ijms24032082.
Early diagnosis of autism spectrum disorder (ASD) is crucial for providing appropriate treatments and parental guidance from an early age. Yet, ASD diagnosis is a lengthy process, in part due to the lack of reliable biomarkers. We recently applied RNA-sequencing of peripheral blood samples from 73 American and Israeli children with ASD and 26 neurotypically developing (NT) children to identify 10 genes with dysregulated blood expression levels in children with ASD. Machine learning (ML) analyzes data by computerized analytical model building and may be applied to building diagnostic tools based on the optimization of large datasets. Here, we present several ML-generated models, based on RNA expression datasets collected during our recently published RNA-seq study, as tentative tools for ASD diagnosis. Using the random forest classifier, two of our proposed models yield an accuracy of 82% in distinguishing children with ASD and NT children. Our proof-of-concept study requires refinement and independent validation by studies with far larger cohorts of children with ASD and NT children and should thus be perceived as starting point for building more accurate ML-based tools. Eventually, such tools may potentially provide an unbiased means to support the early diagnosis of ASD.
早期诊断自闭症谱系障碍（ASD）对于提供早期的适当治疗和家长指导至关重要。然而，ASD 的诊断是一个漫长的过程，部分原因是缺乏可靠的生物标志物。我们最近应用了来自 73 名美国和以色列 ASD 儿童和 26 名神经典型发育（NT）儿童的外周血样本的 RNA 测序，以确定 10 个在 ASD 儿童中血液表达水平失调的基因。机器学习（ML）通过计算机分析模型构建来分析数据，并且可以应用于基于大型数据集优化来构建诊断工具。在这里，我们提出了几个基于我们最近发表的 RNA-seq 研究中收集的 RNA 表达数据集的 ML 生成模型，作为 ASD 诊断的初步工具。使用随机森林分类器，我们提出的两个模型中的两个在区分 ASD 儿童和 NT 儿童方面的准确率达到 82%。我们的概念验证研究需要通过包含更多 ASD 儿童和 NT 儿童的大型队列研究进行细化和独立验证，因此应被视为构建更准确的基于 ML 的工具的起点。最终，此类工具可能为支持 ASD 的早期诊断提供一种公正的手段。
