首页|Northeastern University Reports Findings in Machine Learning (Advances of machine learning-assisted small extracellular vesicles detection strategy)
Northeastern University Reports Findings in Machine Learning (Advances of machine learning-assisted small extracellular vesicles detection strategy)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
New research on Machine Learning is the subject of a report. According to news reporting originating from Shenyang, People’s Republic of China, by NewsRx correspondents, research stated, “Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases.” Our news editors obtained a quote from the research from Northeastern University, “Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal componentlinear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated.”
ShenyangPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesLinear Discriminant AnalysisMachine Learning
NETL
NSTL
