Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

104
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
104

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Target Tissue Identification Based on Image Processing for Regulating Automatic Robotic Lung Biopsy Sampler: Onsite Phantom Validation.

Sensors (Basel, Switzerland)·2026
Same author

Phytoremediation Potential of Heavy Metals Using Biochar and Accumulator Plants: A Sustainable Approach Towards Cleaner Environments.

Plants (Basel, Switzerland)·2025
Same author

Collaborative Heterogeneous Mini-Robotic 3D Printer for Manufacturing Complex Food Structures with Multiple Inks and Curved Deposition Surfaces.

Micromachines·2025
Same author

Systematic review on visual aid technologies for surgical assistant robotic devices<sup></sup>.

Progress in biomedical engineering (Bristol, England)·2025
Same author

Deep Learning Techniques to Characterize the <i>RPS28P7</i> Pseudogene and the <i>Metazoa</i>-<i>SRP</i> Gene as Drug Potential Targets in Pancreatic Cancer Patients.

Biomedicines·2024
Same author

Sequential Treatment by Ozonation and Biodegradation of Pulp and Paper Industry Wastewater to Eliminate Organic Contaminants.

Toxics·2024
Same journal

Precision Proteomic Profiling of Systemic Lupus Erythematosus-Correlating Disease Activity and Complement Levels with Clinical Phenotypes.

Biomedicines·2026
Same journal

The Role of Salivary Microbiota in Pancreatic Cancer: From Screening to Tumor Progression and Treatment Response.

Biomedicines·2026
Same journal

Diagnostic Utility of Surface Electromyography for Identifying Muscles Affected by Myofascial Trigger Points: A Scoping Review.

Biomedicines·2026
Same journal

Performance Assessment of a Locally Semi-Automated NGS-Based Workflow for Homologous Recombination Deficiency Testing in High-Grade Serous Ovarian Carcinoma.

Biomedicines·2026
Same journal

Coupling and Uncoupling Pleiotropy Between Hypertension and Type 2 Diabetes Contribute to Exploring Potential Heterogeneity in Cardiovascular Risk in East Asian Population.

Biomedicines·2026
Same journal

Maternal Response to Therapeutic Plasma Exchange in Early Gestation: A Case Series of Thrombotic Microangiopathies and Neurological Disorders.

Biomedicines·2026
查看所有相关文章

相关实验视频

Updated: Jul 12, 2025

Video Imaging and Spatiotemporal Maps to Analyze Gastrointestinal Motility in Mice
07:41

Video Imaging and Spatiotemporal Maps to Analyze Gastrointestinal Motility in Mice

Published on: February 3, 2016

14.1K

机器学习算法应用于根据肠道微生物组组成预测自闭症谱系障碍.

Juan M Olaguez-Gonzalez1, Isaac Chairez1,2, Luz Breton-Deval3,4

  • 1School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico.

Biomedicines
|October 28, 2023
PubMed
概括
此摘要是机器生成的。

机器学习模型通过分析肠道微生物组合来准确诊断自闭症谱系障碍 (ASD). 这项研究以较少的细菌为重点,解释了相互矛盾的发现,并提高了早期ASD检测准确率高达90%.

关键词:
在ASD中,使用的是ASD.人工神经网络的人工神经网络自闭症自闭症是什么机器学习是机器学习.微生物组是一个微生物组.我们的微生物群.

更多相关视频

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

28.2K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

15.9K

相关实验视频

Last Updated: Jul 12, 2025

Video Imaging and Spatiotemporal Maps to Analyze Gastrointestinal Motility in Mice
07:41

Video Imaging and Spatiotemporal Maps to Analyze Gastrointestinal Motility in Mice

Published on: February 3, 2016

14.1K
Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

28.2K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

15.9K

科学领域:

  • 计算生物学和生物信息学
  • 微生物组研究和分析.
  • 神经发育障碍的诊断神经发育障碍的诊断

背景情况:

  • 肠道微生物组组成越来越多地与自闭症谱系障碍 (ASD) 相关,但之前的研究产生了矛盾的结果.
  • 现有的研究往往忽略了较少的微生物群落,可能缺少关键诊断指标.
  • 机器学习 (ML) 提供了一种强大的方法来分析复杂的生物数据以诊断疾病.

研究的目的:

  • 通过机器学习研究肠道微生物组组成在ASD早期诊断中的作用.
  • 根据微生物组数据,开发和验证ML模型来将个人分类为神经类型 (NT) 或有ASD.
  • 确定与ASD相关的关键微生物预测因素,并解释先前研究中的差异.

主要方法:

  • 应用机器学习算法,包括支持矢量机器 (SVM),人工神经网络 (ANN) 和随机森林 (RF) 进行分类.
  • 利用来自两个独立数据集 (美国和中国) 的已公布的肠道微生物组组成数据 (16S rRNA测序).
  • 训练并验证多个ML模型,重点关注具有最佳性能和可解释性的模型.

主要成果:

  • 实现了高分类准确性 (高达90%),在ASD检测方面具有出色的灵敏度 (96.97%) 和特异性 (85.29%).
  • ANN模型在一个数据集中展示了神经类型受试者的完美分类,突出了显著的诊断潜力.
  • 确定了关键的细菌预测因子,如 *Bacteroides*, *Lachnospira*, *Anaerobutyricum* 和 *Ruminococcus torques*,包括以前被忽视的低丰度微生物.

结论:

  • 机器学习模型有效地利用肠道微生物组数据进行准确的ASD诊断,比传统方法有所改进.
  • 这项研究表明,较少的微生物群落在ASD的发展和诊断中起着至关重要的作用,解释了先前相矛盾的发现.
  • 建议对这些微生物少数群体进行进一步的研究,以完善对ASD的理解和诊断能力.