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

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

Spleen-tonifying formula alleviates social deficits, gut dysbiosis, and hypomyelination in a perinatal injury model.

Pediatrics and neonatology·2026
Same author

Zerumbone from Zingiber zerumbet (L.) Roscoe ex Sm. ameliorates atopic dermatitis by regulating the MAP kinase/NF-κB, Akt, and STAT pathways.

Journal of ethnopharmacology·2026
Same author

Metabolomics Reveals the Anti-hepatic Fibrosis Mechanisms of <i>Pueraria lobata</i> (Willd.) Ohwi Extract and Potential Metabolites Alterations.

International journal of medical sciences·2026
Same author

Integration proteomics analysis to identify AMPK as key target pathways of TCM formula for high fat diet induced obesity in mice.

Journal of traditional and complementary medicine·2026
Same author

Exploring the Therapeutic Potential of Processed Citrus reticulata Peel Extracts in the Treatment of Prostate Cancer and Benign Prostatic Hyperplasia.

Current pharmaceutical design·2026
Same author

Interactions between turnover rate and bioactivity of atractylenolide III in Atractylodes macrocephala rhizome in the theory of crude drugs' pairs and prescriptions in traditional Chinese and Japanese Kampo medicine.

Journal of natural medicines·2025

相关实验视频

Updated: Jul 13, 2025

OLIgo Mass Profiling OLIMP of Extracellular Polysaccharides
08:43

OLIgo Mass Profiling OLIMP of Extracellular Polysaccharides

Published on: June 20, 2010

13.8K

基于机器学习的单糖样分析用于Wolfiporia extensa样本的组织特异分类.

Shih-Yi Hsiung1, Shun-Xin Deng2, Jing Li3

  • 1School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.

Carbohydrate polymers
|October 15, 2023
PubMed
概括

机器学习模型使用单糖形状准确地对Wolfiporia extensa组织类型进行了分类. 这些先进的生物信息学方法超过了用于识别真菌组织的传统技术.

关键词:
线性差异分析线性差异分析机器学习 机器学习预测模型是一个预测模型.组织特异性分类组织特异性分类.这种植物是Wolfiporia extensa

更多相关视频

Author Spotlight: MAPP Protocol &#8211; Advancing Glycan Analysis
07:12

Author Spotlight: MAPP Protocol – Advancing Glycan Analysis

Published on: September 29, 2023

1.8K
Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis
12:02

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis

Published on: April 11, 2016

11.5K

相关实验视频

Last Updated: Jul 13, 2025

OLIgo Mass Profiling OLIMP of Extracellular Polysaccharides
08:43

OLIgo Mass Profiling OLIMP of Extracellular Polysaccharides

Published on: June 20, 2010

13.8K
Author Spotlight: MAPP Protocol &#8211; Advancing Glycan Analysis
07:12

Author Spotlight: MAPP Protocol – Advancing Glycan Analysis

Published on: September 29, 2023

1.8K
Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis
12:02

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis

Published on: April 11, 2016

11.5K

科学领域:

  • 生物信息学是一种生物信息学.
  • 菌类学 菌类学是指菌类学.
  • 计算生物学 计算生物学

背景情况:

  • 机器学习 (ML) 在生物信息学中越来越多地用于临床决策和诊断.
  • 准确识别真菌组织类型对于研究和应用至关重要.

研究的目的:

  • 评估八种机器学习算法的有效性,用于根据单糖化合物组成对Wolfiporia extensa的四种组织类型进行分类.
  • 将ML模型的性能与线性差异分析 (LDA) 等传统方法进行比较.

主要方法:

  • 测试了八种机器学习算法:线性差别分析 (LDA),逻辑回归 (LR),k-最近邻居 (KNN),随机森林 (RF),梯度增强机 (GBM),支持向量机 (SVM),天真贝叶斯分类器 (NB) 和人工神经网络 (ANN).
  • 作为输入特征,使用了Wolfiporia extensa组织的单糖化合物组成概况.
  • 用曲线下的面积 (AUC) 度量来评估分类和预测能力.

主要成果:

  • 所有八种ML模型都表现出示例性的性能,AUC>0.8用于组织分类.
  • 五种模型 (LDA,KNN,RF,GBM,ANN) 实现了四种组织类型的优异分类准确性 (AUC>0.9).
  • 所有八种模型都显示了三种组织类型的良好预测性能 (AUC > 0.8).
  • 基于ML的方法显著优于传统的LDA绘图方法.

结论:

  • 机器学习算法提供了强大的和准确的方法来分类Wolfiporia extensa组织类型使用单糖组成.
  • 与传统的回归技术相比,ML方法提供了更高的性能,特别是对于大型数据集.
  • 这些发现凸显了ML在生物信息学中提高真菌组织识别准确性的潜力.