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相关概念视频

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.3K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.3K
Genetic Material01:20

Genetic Material

3.2K
Within the human body, a complex and detailed system of trillions of cells works in unison to sustain life. Each cell houses a nucleus, which contains 46 chromosomes divided into 23 pairs. Chromosomes are highly coiled structures made of the genetic material DNA. These chromosomes are essential carriers of genetic information, with half inherited from the mother through her egg and the other half from the father's sperm, combining to create the unique genetic makeup of an individual.
3.2K
Genetic Screens02:46

Genetic Screens

5.6K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.6K
Randomized Experiments01:13

Randomized Experiments

8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.8K
Types of Genetic Transfer Between Organisms02:18

Types of Genetic Transfer Between Organisms

6.2K
6.2K
Types of Genetic Transfer Between Organisms02:18

Types of Genetic Transfer Between Organisms

30.5K
Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.
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相关实验视频

Updated: Jan 14, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

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安全的多方测试案例数据生成通过生成的对抗网络.

Zheng Wang1, Liutao Zhao2, Fanyin Meng3

  • 1Institute of Digital Economy, Beijing Academy of Science and Technology, Beijing, 100032, China.

Scientific reports
|January 12, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种保护隐私的生成对抗网络 (GAN),用于联合测试案例数据生成. 该方法在分布式环境中提高了软件测试效率和数据质量.

关键词:
自动编码器 自动编码器联合学习学习 (Federated Learning) 是一种联合学习.生成性的对抗性网络.测试案例的生成

相关实验视频

Last Updated: Jan 14, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.5K

科学领域:

  • 软件工程 软件工程 软件工程
  • 人工智能的人工智能
  • 数据 隐私 数据 隐私 数据

背景情况:

  • 软件测试案例数据生成的挑战包括质量差和合成困难.
  • 跨组织的分布式数据和隐私问题阻碍了集中式数据聚合.
  • 联合学习环境对于安全的,跨组织的数据处理是必要的.

研究的目的:

  • 提出一种基于生成对抗网络 (GAN) 的方法,用于保护隐私,联合测试案例数据生成.
  • 解决分布式软件测试中的数据质量,合成和隐私挑战.

主要方法:

  • 一种基于GAN的方法,使用协议语法深度学习框架.
  • 测试案例编码器-解码器机制和GAN驱动的样本字符生成器.
  • 发电机和区分器的本地培训,在联合环境中安全聚合模型参数.

主要成果:

  • 拟议的方法产生高质量,多样化的测试案例数据,同时保持隐私.
  • 与传统方法相比,生成的数据显示了更高的覆盖率和有效性.
  • 显著提高软件测试的效率和质量.

结论:

  • 该框架提供了一个可扩展的解决方案,用于在联合环境中生成测试数据.
  • 在跨组织环境中有效解决数据主权要求.
  • 帮助识别关键基础设施中的潜在漏洞.