Garbulowski Mateusz, Smolinska Karolina, Diamanti Klev, Pan Gang, Maqbool Khurram, Feuk Lars, Komorowski Jan
Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Front Genet. 2021 Feb 25;12:618277. doi: 10.3389/fgene.2021.618277. eCollection 2021.
Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between and , which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.
自闭症谱系障碍（ASD）是一种具有复杂遗传背景的异质性神经精神疾病。分析ASD患者中改变的分子过程需要提供可解释解决方案的线性和非线性方法。可解释的机器学习提供了清晰的模型，能够解释生物学机制并支持临床亚组分析。在这项工作中，我们研究了几项关于ASD个体基因表达测量的病例对照研究。我们从三个独立的数据集中构建了一个基于规则的学习模型，并将其进一步可视化为一个非线性基因-基因共预测网络。为了找出ASD亚型之间的差异，我们仔细研究了网络的拓扑结构并估计了中心性距离。我们的分析表明，自闭症是ASD中最严重的亚型，而未特定指明的广泛性发育障碍和阿斯伯格综合征密切相关且是较轻的ASD亚型。此外，我们分析了根据基因共预测因子描述的最重要的ASD相关特征。其中，我们发现[此处原文缺失具体基因信息]和[此处原文缺失具体基因信息]之间存在强大的共预测机制，这可能表明这些基因之间存在共同调控。本研究证明了在生物信息学分析中应用可解释机器学习的潜力。尽管所提出的方法是为转录组学数据设计的，但它也可应用于其他组学学科。
