通过机器学习分析表面增强拉曼光谱快速鉴别临床重要病原体的表2

数据集获取（Figshare）

• 源站标题：Table_2_Rapid Discrimination of Clinically Important Pathogens Through Machine Learning Analysis of Surface Enhanced Raman Spectra.DOCX
• DOI：10.3389/fmicb.2022.843417.s002
• 发布日期：2022-04-08
• 文件数：1 个
• 文件总体积：14.55 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Rapid_Discrimination_of_Clinically_Important_Pathogens_Through_Machine_Learning_Analysis_of_Surface_Enhanced_Raman_Spectra_DOCX/19547884

命令行下载

curl -L -o "Table_2_Rapid Discrimination of Clinically Important Pathogens Through Machine Learning Analysis of Surface Enhanced Raman Spectra.DOCX" "https://ndownloader.figshare.com/files/34734616"

文件清单

• Table_2_Rapid Discrimination of Clinically Important Pathogens Through Machine Learning Analysis of Surface Enhanced Raman Spectra.DOCX（14.55 KB）

文件总体积 14.55 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

With its low-cost, label-free and non-destructive features, Raman spectroscopy is becoming an attractive technique with high potential to discriminate the causative agent of bacterial infections and bacterial infections per se. However, it is challenging to achieve consistency and accuracy of Raman spectra from numerous bacterial species and phenotypes, which significantly hinders the practical application of the technique. In this study, we analyzed surfaced enhanced Raman spectra (SERS) through machine learning algorithms in order to discriminate bacterial pathogens quickly and accurately. Two unsupervised machine learning methods, K-means Clustering (K-Means) and Agglomerative Nesting (AGNES) were performed for clustering analysis. In addition, eight supervised machine learning methods were compared in terms of bacterial predictions via Raman spectra, which showed that convolutional neural network (CNN) achieved the best prediction accuracy (99.86%) with the highest area (0.9996) under receiver operating characteristic curve (ROC). In sum, machine learning methods can be potentially applied to classify and predict bacterial pathogens via Raman spectra at general level.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmicb.2022.843417.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Rapid_Discrimination_of_Clinically_Important_Pathogens_Through_Machine_Learning_Analysis_of_Surface_Enhanced_Raman_Spectra_DOCX/19547884）。