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相关概念视频

Sampling Methods: Overview01:06

Sampling Methods: Overview

2.6K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
2.6K
Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.0K
Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
896
Upsampling01:22

Upsampling

583
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
583
Bootstrapping01:24

Bootstrapping

810
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
810
Systematic Sampling Method01:17

Systematic Sampling Method

12.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
12.5K

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相关实验视频

Updated: Jan 16, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
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An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

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对于大数据集的低采样技术.

Lexin Chen1,2, Ramón Alain Miranda-Quintana1,2

  • 1Department of Chemistry, University of Florida, Gainesville, Florida 32611, USA.

bioRxiv : the preprint server for biology
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

用DNA编码的图书馆 (DEL) 为药物发现产生了庞大的化学图书馆. 本研究通过评估低采样技术来改善机器学习模型培训,以解决DEL数据中的类不平衡问题.

关键词:
算法算法是一种算法.集群化学是一种集群化学.分子模拟分子模拟

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Last Updated: Jan 16, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

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Sampling Soils in a Heterogeneous Research Plot
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科学领域:

  • 药用化学 医学化学
  • 化学信息学是一种化学信息学.
  • 机器学习 机器学习

背景情况:

  • DNA编码图书馆 (DEL) 能够快速合成和选数十亿个小分子.
  • 机器学习 (ML) 模型从药物发现的DEL绑定数据中受益.
  • 类不平衡,其中的无活性化合物远远超过活性化合物,对DEL中的ML模型培训构成重大挑战.

研究的目的:

  • 调查和对DEL数据集中多数 (非活跃) 类的各种下样本策略进行比较.
  • 评估这些策略对在不平衡的DEL数据上训练的ML模型的性能的影响.

主要方法:

  • 对大多数类别的不同亚抽样技术的探索.
  • 对随机选择进行基准测试,对比下面的抽样策略.
  • 在两个不同的DEL数据集上进行原型设计和评估.
  • 用三种不同的机器学习模型进行测试.

主要成果:

  • "max_sim"低采样策略在评估的指标中表现出卓越的表现.
  • 对比分析显示,在处理阶级不平衡方面,与随机选择相比,有显著的改善.
  • 开发的管道在DELight包中成功实施.

结论:

  • 低采样策略,特别是"max_sim",可以有效地缓解DEL数据集中的类不平衡.
  • 使用平衡的DEL数据改进的ML模型训练可以增强成功识别和药物发现工作.
  • 基于DEL的药物发现中,DELight包为应用这些策略提供了一个实用的工具.