Visual Prediction of the Progression of Spinocerebellar Ataxia Type 3 Based on Machine Learning

IF 2.4 3区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS Current Bioinformatics Pub Date : 2023-07-10 DOI:10.2174/1574893618666230710140505
R. Qiu, Danlei Ru, Jinchen Li, Linliu Peng, Hong Jiang
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Abstract

Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is a clinically heterogeneous and progressive condition. Evaluation of its progression will contribute to clinical management and genetic counseling. The objective of this study was to provide a visualized interpretable prediction of the progression of SCA3/MJD based on machine learning (ML) methods. A total of 716 patients with SCA3/MJD were included in this study. The International Cooperative Ataxia Rating Scale (ICARS) and Scale for the Assessment and Rating of Ataxia (SARA) scores were used to quantitatively assess disease progression in the patients. Clinical and genotype information were collected as factors for predicting progression. Prediction models were constructed with ML algorithms, and the prediction results were then visualized to facilitate personalizing of clinical consultation. The CAG repeat length of ATXN3 and its product with age, the duration of disease, and age were identified as the 4 most important factors for predicting the severity and progression of SCA3/MJD. The SVM-based model achieved the best performance in predicting the total ICARS and SARA scores, with accuracy (10%) values of 0.7619 for the SARA and 0.7042 for the ICARS. To visualize the predictions, line charts were used to show the expected progression over the next decade, and radar charts were used to show the scores of each part of the ICARS and SARA separately. We are the first group to apply ML algorithms to predict progression in SCA3/MJD and achieved desirable results. Visualization provided personalized predictions for each sample and can aid in developing clinical counseling regimens in the future.

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基于机器学习的脊髓小脑共济失调3型进展的视觉预测
脊髓小脑性共济失调3型/Machado-Joseph病(SCA3/MJD)是一种临床异质性进行性疾病。评估其进展将有助于临床管理和遗传咨询。本研究的目的是基于机器学习(ML)方法提供SCA3/MJD进展的可视化可解释预测。本研究共纳入716例SCA3/MJD患者。采用国际合作共济失调评定量表(ICARS)和共济失调评定评定量表(SARA)评分对患者的疾病进展进行定量评定。收集临床和基因型信息作为预测进展的因素。利用ML算法构建预测模型,并对预测结果进行可视化处理,便于临床会诊个性化。ATXN3 CAG重复序列长度及其与年龄、病程和年龄的乘积是预测SCA3/MJD严重程度和进展的4个最重要的因素。基于svm的模型在预测ICARS和SARA总分方面表现最佳,SARA的准确率(10%)为0.7619,ICARS的准确率为0.7042。为了使预测可视化,使用折线图来显示未来十年的预期进展,使用雷达图分别显示ICARS和SARA的每个部分的分数。我们是第一个应用ML算法预测SCA3/MJD进展的团队,并取得了理想的结果。可视化为每个样本提供了个性化的预测,并有助于未来开发临床咨询方案。
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来源期刊
Current Bioinformatics
Current Bioinformatics 生物-生化研究方法
CiteScore
6.60
自引率
2.50%
发文量
77
审稿时长
>12 weeks
期刊介绍: Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
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