Development of a miRNA-based deep learning model for autism spectrum disorder diagnosis

Dogra, Saanvi1 ; Kouznetsova, Valentina L.2,3,4 ; Kesari, Santosh5 ; Tsigelny, Igor F.2,3,4,6,*

1REHS Program, San Diego Supercomputer Center, UC San Diego, La Jolla, CA, USA

2San Diego Supercomputer Center, UC San Diego, La Jolla, CA, USA

3BiAna Institute, San Diego, CA, USA

4CureScience Institute, San Diego, CA, USA

5Pacific Neuroscience Institute, Santa Monica, CA, USA

6Department of Neurosciences, UC San Diego, La Jolla, CA, USA

*Correspondence to: Igor F. Tsigelny, PhD, itsigeln@ucsd.edu.

Advanced Technology in Neuroscience 2(2):p 72-76, June 2025. | DOI: 10.4103/ATN.ATN-D-24-00033

Autism spectrum disorder is a neurodevelopmental disorder characterized by differences in social behaviors, intellectual disabilities, and various mental health conditions. It is often undiagnosed due to overlapping symptoms with other disorders and the challenging, subjective nature of behavioral analysis. However, recent studies have identified dysregulated microRNAs as potential biomarkers for autism spectrum disorder, which could enable more accurate quantitative diagnoses. This study aimed to develop a machine learning model to predict whether dysregulation of a specific miRNA is associated with autism spectrum disorder. We selected an even number of autism spectrum disorder-associated miRNAs and randomly chosen miRNAs for analysis. Data was collected on amino acid sequences, gene targets, and predicted pathway attributes to classify each microRNA. Feature selection was then performed to identify the optimal number of features for achieving the highest accuracy. Only statistically significant predictions (P < 0.05) were included in the training dataset. The sequential model with two hidden layers emerged as the best classifier, achieving an accuracy of 95.24% for microRNA biomarkers. This model was further validated with an independent, unseen dataset, which achieved 81.67% accuracy. The study also explored the genes and pathways of significance to understand better potential causes of autism spectrum disorder, particularly those involved in regulating the pluripotency of stem cells. This study presents a rapid and efficient method for classifying microRNAs as potential biomarkers for autism spectrum disorder based on their biological characteristics. By screening for dysregulated microRNAs in patients’ blood or serum samples, this approach can enhance early diagnosis and timely intervention.

中文摘要

自闭症谱系障碍是一种神经发育障碍，其特征是社交行为差异、智力障碍和各种心理健康问题。由于其症状与其他疾病重叠，且行为分析具有挑战性和主观性，自闭症谱系障碍常常无法确诊。然而，最近的研究已将失调的microRNAs鉴定为自闭症谱系障碍的潜在生物标志物，从而有助于更准确的定量诊断。本研究旨在开发一种机器学习模型，用于预测特定miRNA的失调是否与自闭症谱系障碍相关。我们选择了偶数个与自闭症谱系障碍相关的miRNAs和随机选择的miRNAs进行分析。收集了氨基酸序列、基因靶点和预测通路属性的数据，以对每个microRNA进行分类。然后进行特征选择，以确定实现最高精度的最佳特征数量。训练数据集仅包含具有统计学意义的预测值（P < 0.05）。具有两个隐藏层的顺序模型被评为最佳分类器，其microRNA生物标志物的准确率高达95.24%。该模型进一步利用独立的、未见的数据集进行了验证，准确率达到 81.67%。本研究还探索了重要的基因和通路，以更好地理解自闭症谱系障碍的潜在病因，特别是那些参与调控干细胞多能性的基因和通路。本研究提出了一种快速有效的方法，可根据microRNA的生物学特性将其分类为自闭症谱系障碍的潜在生物标记物。通过筛查患者血液或血清样本中失调的microRNA，该方法可以增强早期诊断和及时干预。