Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.

Briefings in bioinformatics·2025
Same author

Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.

bioRxiv : the preprint server for biology·2025
Same author

Clinico-genomic features predict distinct metastatic phenotypes in cutaneous melanoma.

bioRxiv : the preprint server for biology·2025
Same author

Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic data.

ArXiv·2025
Same author

Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanoma.

Science advances·2024
Same author

Platinum Cluster Decoration on Hollow Carbon Spheres for High-Efficiency Hydrogen Evolution Reaction.

Langmuir : the ACS journal of surfaces and colloids·2024

相关实验视频

Updated: Sep 16, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.0K

规范化和选择非差异表达基因 改善跨平台转录基因数据的机器学习建模

Fei Deng1, Catherine H Feng1,2, Nan Gao3,4

  • 1Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA.

Transactions on artificial intelligence
|July 9, 2025
PubMed
概括

非差异表达基因 (NDEG) 改善了转录基因数据的正常化,提高了对独立RNA微阵列和RNA-seq数据集的机器学习模型性能,用于乳腺癌亚型分类.

关键词:
乳腺癌 乳腺癌 乳腺癌功能选择 功能选择机器学习是机器学习.规范化的正常化.转录组学 转录组学是指转录组学.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

914
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.4K

相关实验视频

Last Updated: Sep 16, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.0K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

914
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.4K

科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 生物过程的定量分析需要强大的规范化.
  • 机器学习 (ML) 的RNA微阵列和RNA测序 (RNA-seq) 数据的跨平台集成是具有挑战性的,因为缺乏独立的验证.
  • 在跨平台的独立数据集上提高ML模型性能至关重要.

研究的目的:

  • 测试非差异表达基因 (NDEG) 可以改善转录基因数据规范化和跨平台ML建模的假设.
  • 通过独立的微阵列和RNA-seq数据集来评估基于NDEG的规范化,用于分类乳腺癌分子亚型.

主要方法:

  • 使用TCGA乳腺癌微阵列和RNA-seq数据集作为独立的培训和测试集.
  • 根据ANOVA的p值,选择了NDEG (p > 0.85) 和差异表达基因 (DEG) (p < 0.05).
  • 应用NDEG用于规范化和DEG用于ML模型中的分类,测试跨平台性能.

主要成果:

  • NDEG和DEG选择显著改善了ML模型分类性能.
  • 非参数统计规范化方法的性能优于参数方法.
  • 使用神经网络的LOG_QN和LOG_QNZ规范化显示出卓越的性能.

结论:

  • 基于NDEG的规范化对于独立数据集的跨平台ML模型测试是有效的.
  • 这种方法对改进转录基因数据分析和分类有希望.
  • 需要进一步的研究来验证跨不同数据集和omics类型的NDEG规范化.