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

Cancer Survival Analysis01:21

Cancer Survival Analysis

394
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
394
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K

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

Benchmarking computational methods for multi-omics biomarker discovery in cancer.

Briefings in bioinformatics·2026
Same author

The implications of alternative splicing regulation for maximum lifespan.

Nature communications·2025
Same author

The Implications of Alternative Splicing Regulation for Maximum Lifespan.

bioRxiv : the preprint server for biology·2025
Same author

Shiba: a versatile computational method for systematic identification of differential RNA splicing across platforms.

Nucleic acids research·2025
Same author

Deciphering single-cell gene expression variability and its role in drug response.

Human molecular genetics·2024
Same author

Shiba: A versatile computational method for systematic identification of differential RNA splicing across platforms.

bioRxiv : the preprint server for biology·2024

相关实验视频

Updated: Jul 23, 2025

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

807

基于个人转录组的可解释深度学习,以改善癌症患者的生存率.

Bo Sun1, Liang Chen2

  • 1Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA, 90089, USA.

Scientific reports
|July 13, 2023
PubMed
概括

这项研究介绍了CancerIDP,一种可解释的深度学习模型,使用药物处方和转录组预测癌症患者的生存率. 该模型准确地确定了生存结果,并建议替代药物,可能增加中位生存时间.

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

6.6K

相关实验视频

Last Updated: Jul 23, 2025

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

807
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

6.6K

科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 精准医学旨在通过考虑个体患者的变化来个性化治疗.
  • 癌症研究产生了大量的数据,包括基因组和药物处方信息.
  • 预测患者的生存率对于治疗计划和药物开发至关重要.

研究的目的:

  • 开发一个可解释的深度学习模型 (CancerIDP) 来预测癌症患者的生存率.
  • 利用药物处方和个人转录组作为存活预测的输入特征.
  • 确定可能改善患者生存结果的潜在替代药物.

主要方法:

  • 开发一种可解释的神经网络模型,命名为CancerIDP.
  • 输入特征包括患者的药物处方和转录组数据.
  • 用分类准确度和皮尔森相关性来预测生存时间的模型性能.

主要成果:

  • 癌症IDP在区分短寿和长寿癌症患者方面实现了96%的准确性.
  • 在预测和实际死亡前几个月之间观察到0.937的高皮尔森相关性.
  • 该模型确定了可能使27.4%的患者受益的替代药物,使中位数存活时间增加3.9个月.

结论:

  • 可解释的深度学习模型CancerIDP有效地预测了癌症患者的生存率.
  • 使用癌症IDP的个性化医疗方法可以确定替代治疗方法以改善生存率.
  • 该模型的可解释性为开发新型癌症药物的机制研究提供了便利.