相关实验视频

Updated: Jan 20, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K

机器学习驱动的纳米孔传感用于定量,无标签的miRNA检测

Caroline Koch1,2, Seshagiri Sakthimani1, Victoria Maria Noakes1

  • 1Department of Chemistry, Molecular Science Research Hub, Imperial College London, London, UK.

Small methods
|January 19, 2026
PubMed
概括

我们开发了一种纳米孔传感器测试,使用DNA条形码探针进行敏感的微RNA检测. 与传统方法相比,卷积神经网络 (CNN) 显著提高了诊断准确性.

关键词:
生物标志物生物标志物数据分析数据分析机器学习就是机器学习.这就是miRNAs.纳米孔是一种纳米孔.

更多相关视频

Asthma Detection Research Based on Voice Signal Processing and Machine Learning04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

939
Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography10:40

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

1.5K

相关概念视频

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell...
13.0K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and...
939
Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography10:40

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

We present a step-by-step protocol for high-resolution, label-free, and three-dimensional imaging of organoids using low-coherence holotomography. This protocol details organoid culture preparation, imaging acquisition, and computational image analysis, enabling real-time visualization of structural dynamics and drug responses in living...
1.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...
7.4K
Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores09:43

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

A methodology for preparing solid-state nanopores in solution for biomolecular translocation experiments is presented. By applying short pulses of high electric fields, the nanopore diameter can be controllably enlarged with subnanometer precision and its electrical noise characteristics significantly improved. This procedure is performed in situ using standard laboratory equipment under experimental...
14.1K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic...
4.4K