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

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

Pathology-Informed Augmentation Improves Cross-Cohort IMU-to-vGRF Estimation Between Healthy Adults and Adults With Osteoarthritis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

QeITH: Quantifies Tumor Ecosystem Heterogeneity to Predict Cancer Progression and Treatment Benefit.

Computational and structural biotechnology journal·2026
Same author

Correction: Pseudolaric acid B induces G2/M phase arrest in canine mammary tumor cells by targeting CDK1.

Frontiers in veterinary science·2026
Same author

ScSpTITH: a rank-correlation framework for robust quantification of multi-dimensional tumor heterogeneity.

Human genetics·2026
Same author

Cost-effectiveness analysis of the treatment pathway after trastuzumab treatment failure in patients with HER2-positive advanced breast cancer: a chinese health system perspective.

BMC health services research·2026
Same author

Single-Cell Dissection of Malignant Cell Heterogeneity Reveals Functional Programs and Clinically Relevant Subtypes in Head and Neck Squamous Cell Carcinoma.

IUBMB life·2026

相关实验视频

Updated: May 28, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.1K

使用基于统一的机器学习框架MoTP的多omics数据推断瘤纯度.

Qiqi Lu1,2,3, Zhixian Liu4, Xiaosheng Wang1,2,3

  • 1Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.

Briefings in bioinformatics
|February 14, 2025
PubMed
概括

多omics瘤纯度预测 (MoTP) 算法集成了多个omics数据类型,以更准确地估计瘤纯度. 这种机器学习方法的性能优于现有的方法,为癌症研究提供了强大的和高效的计算工具.

关键词:
算法算法是一种算法.机器学习是机器学习.奥米克斯数据数据的数据.预测瘤纯度 预测瘤纯度

更多相关视频

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
07:47

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

1.4K
Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis
12:41

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis

Published on: December 23, 2022

4.7K

相关实验视频

Last Updated: May 28, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.1K
Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
07:47

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

1.4K
Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis
12:41

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis

Published on: December 23, 2022

4.7K

科学领域:

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

背景情况:

  • 目前的瘤纯度评估算法仅限于单个omics数据类型.
  • 这种局限性阻碍了对瘤纯度的全面和准确估计.

研究的目的:

  • 开发和验证多omics瘤纯度预测 (MoTP) 算法,以提高瘤纯度估计.
  • 为了利用多个omics数据类型来提高预测性能.

主要方法:

  • 利用贝叶斯规范神经网络进行预测.
  • 从21种TCGA癌症类型中使用集成的多omics数据 (mRNA,microRNA,lncRNA,DNA甲基化) 进行训练的MoTP.
  • 在TCGA和外部数据集上验证的MoTP,包括那些带有噪音和缺失特征的数据集.

主要成果:

  • MoTP表现出色,多omics集成显著提高了对单个omics数据的预测准确性.
  • 该算法在杂和不完整的数据集上进行测试时显示出强度.
  • 基准分析证实了MoTP的优越性能与既定算法相比,计算要求降低.

结论:

  • MoTP提供了一种强大而高效的机器学习方法,用于计算瘤纯度推断.
  • 整合多omics数据提供了更准确和可靠的瘤纯度估计.
  • MoTP代表了癌症研究和临床应用的有吸引力的进步.