Jove
Visualize
联系我们

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

CoNutriNet: a dual-branch architecture with DenseNet and graph-enhanced attention network for coffee nutrient deficiency classification.

Frontiers in plant science·2026
Same author

Extraction of natural fibres from Agave fourcroydes leaves and multi-property evaluation for potential textile applications.

Scientific reports·2026
Same author

Assessment of chemotherapy dosing and regimen optimization in geriatric oncology patients diagnosed with solid organ malignancies.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners·2026
Same author

BoneVisionNet: A deep learning approach for the classification of bone tumours from radiographs using a triple fusion attention network of transformer and CNNs with XAI visualizations.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2025
Same author

Health-Related Quality of Life (HRQoL) Among Adults With Non-communicable Diseases in a Selected District of South India: A Cross-Sectional Study.

Cureus·2025
Same author

Impact of peak expiratory flow rate in general and regional anesthesia: A comparative study.

Bioinformation·2025
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

相关实验视频

Updated: May 22, 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

616

修改量子扩展卷积神经网络用于癌症预测,使用基因表达数据.

Magendiran N1, Karthik R2, Dhanalakshmi V3

  • 1Department of Computer Science and Technology, Vivekanandha College of Engineering for Women (Autonomous), Tiruchengode, Namakkal, Tamil Nadu, India.

Computer methods in biomechanics and biomedical engineering
|May 20, 2025
PubMed
概括

这项研究引入了经过修改的量子扩展卷积神经网络 (QDCNN),用于使用基因表达数据准确检测癌症,达到90.6%的准确率. 该模型有效预测癌症,在计算瘤学方面提供了有前途的进展.

关键词:
基因表达数据 基因表达数据库尔斯基 (Kulczynski) 是一个著名的作家.适应性的Box-Cox转换方式角度分离距离距离的距离.功能融合功能融合功能

更多相关视频

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.5K
Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

11.8K

相关实验视频

Last Updated: May 22, 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

616
Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.5K
Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

11.8K

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.
  • 机器学习在医疗保健中的应用

背景情况:

  • 基因表达数据对于理解癌症生物学至关重要.
  • 从基因表达数据准确检测癌症仍然是一个挑战.
  • 深度学习模型显示了分析复杂生物数据集的潜力.

研究的目的:

  • 提出一个修改的量子扩展卷积神经网络 (QDCNN),用于增强癌症检测.
  • 利用基因表达数据来提高诊断准确度.
  • 评估拟议的QDCNN模型在癌症数据集上的性能.

主要方法:

  • 利用了来自PANCAN数据集的基因表达数据.
  • 应用适应式Box-Cox转换用于数据预处理.
  • 与Kulczynski一起使用深度神经网络 (DNN) 进行特征融合.
  • 将精细的特征入改进的QDCNN,用于癌症预测.

主要成果:

  • 经过修改的QDCNN实现了90.6%的准确性.
  • 实现了89.0%的真实阳性率 (TPR).
  • 报告了0.109的虚假负率 (FNR) 和89.9%的马修斯相关系数 (MCC).

结论:

  • 修改后的QDCNN在从基因表达数据中检测癌症方面表现出高效率.
  • 拟议的方法在癌症预测准确度上有了显著的改进.
  • 这种方法有可能在癌症诊断中得到临床应用.