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

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

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...

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

Benefits of building information modeling (BIM) for construction projects success in Pakistan.

Scientific reports·2026
Same author

An ensemble-based sentiment analysis approach for precision medicine recommendation.

Scientific reports·2026
Same author

Isolated Bilateral Congenital Lower Lip Sinuses in a Child: A Rare Non-syndromic Presentation.

Journal of Indian Association of Pediatric Surgeons·2026
Same author

Nomogram of Common Bile Duct Diameter in Children from a Tertiary Care Center in Central India: A Cross-sectional Observational Study.

Journal of Indian Association of Pediatric Surgeons·2026
Same author

Impact of Project Communication on Project Success (Health Projects): Mediating Role of Work Engagement.

Journal of epidemiology and global health·2026
Same author

Explainable federated transformer framework for joint leukemia classification and stage prediction.

Scientific reports·2026
Same journal

Quality Appraisal of Telerehabilitation Guidelines: A Systematic Review.

International journal of telemedicine and applications·2026
Same journal

Application of Digital Twin Technology to Enhance Chronic Diseases Management: A Systematic Review.

International journal of telemedicine and applications·2026
Same journal

Delivering Health Coaching in a Student-Led Telehealth Clinic: Evaluating the Impact on Health-Related Quality of Life.

International journal of telemedicine and applications·2026
Same journal

AI-Based Intraoral Videography for Automated Dental Inspection and Charting in Children With Mixed Dentition.

International journal of telemedicine and applications·2026
Same journal

Perspectives of Rehabilitation Specialists on Telerehabilitation in Jordan: Knowledge, Attitudes, and Implementation Barriers.

International journal of telemedicine and applications·2026
Same journal

CERV-Score: A Hybrid Machine Learning Framework for Cervical Cancer Risk Prediction Using Integrated Clinical and Genomic Data.

International journal of telemedicine and applications·2026
查看所有相关文章

相关实验视频

Updated: Jun 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

集成分类模型与基于CFS-IGWO的特征选择,用于使用微阵列数据检测癌症.

Pinakshi Panda1, Sukant Kishoro Bisoy1, Sandeep Kautish2

  • 1Department of Computer Science & Engineering, C. V. Raman Global University, Bidyanagar, Mahura, Janla 752054, Bhubaneswar, Odisha, India.

International journal of telemedicine and applications
|October 25, 2024
PubMed
概括
此摘要是机器生成的。

机器学习有助于利用基因表达和微阵列数据早期发现癌症. 组合方法,特别是多数投票,在提高癌症预测的诊断准确性方面表现优越.

关键词:
癌症 癌症 癌症 癌症 癌症相关性特征选择 (CFS)改进的灰狼优化器 (IGWO)微阵列的微阵列

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
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

645

相关实验视频

Last Updated: Jun 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
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

645

科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 癌症是全球主要的死亡原因,需要在早期检测方面取得进展.
  • 机器学习 (ML) 为早期癌症诊断提供了有前途的方法,利用基因表达和微阵列数据.
  • 在ML模型中的高维数据,在基因表达和微阵列数据集中很常见,可以降低效率.

研究的目的:

  • 提出和评估两种组合技术,以改进基于ML的癌症诊断.
  • 调查相关性特征选择 (CFS) 和改进的灰狼优化器 (IGWO) 在特征选择和优化方面的有效性.
  • 为了比较多数投票和加权平均组合方法的性能.

主要方法:

  • 用于ML模型训练的基因表达和微阵列数据.
  • 应用相关性特征选择 (CFS) 用于特征选择和改进的灰狼优化器 (IGWO) 用于特征优化.
  • 实施组合技术 (多数投票和加权平均) 来结合来自各种分类器的预测,包括SVM,MLP,LR,DT,AdaBoost,ELM和KNN.

主要成果:

  • 使用精度 (ACC),特异性 (SPE),灵敏度 (SEN),精度 (PRE),马修斯相关系数 (MCC) 和F1得分 (F1-S) 评估模型性能.
  • 大多数投票组合技术与加权平均组合技术相比,表现优越.
  • 特性选择和优化方法 (CFS和IGWO) 对于处理高维数据至关重要.

结论:

  • 组合方法,特别是多数投票,显著提高了基于ML的癌症诊断的准确性.
  • 有效的特征选择和优化对于在癌症研究中管理高维的奥米克数据至关重要.
  • 拟议的方法为早期癌症检测提供了一个强大的策略,可能降低死亡率.