Purpose

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Design/methodology/approach

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

Findings

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

Originality/value

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

中文翻译：

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大数据方法可开发一种全面而准确的工具，旨在改善自闭症谱系障碍的诊断和亚型分层

目的

自闭症谱系障碍（ASD）是一种复杂的神经发育障碍，由于其异质的临床表现而难以准确诊断。结合不同大数据方法（例如，神经影像学，遗传学，眼动追踪等）的综合模型可能会提供从多个不同角度表征ASD的机会。本文旨在概述基于这些大数据方法的ASD分类和分层的新型诊断方法。

设计/方法/方法

连续三年收集并记录了多种类型的数据，包括临床评估，神经影像学，基因突变以及表达和反应信号数据。作者建议通过整合各种数据类型来建立用于预测ASD临床诊断状态的分类模型。此外，作者们提出了一种数据驱动的方法，可根据遗传和基因组数据将ASD分为亚型。

发现

通过利用来自不同类型的ASD患者数据的补充信息，与仅关注一种数据类型的模型相比，所提出的集成模型具有实现更好的预测性能的潜力。在基于基因的数据驱动分层中使用无监督聚类将能够识别更多同质亚型。作者预计，这样的分层将促进更加一致和个性化的ASD诊断工具。

创意/价值

这项研究的目的是比以前的调查更全面地调查与ASD相关的数据类型，包括提出纵向数据收集和覆盖不同人群的存储方案。此外，这项研究提供了两个新的诊断模型，这些模型集中于病例控制状态预测和ASD亚型分层，这在现有文献中尚未得到充分研究。