Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis.

Briefings in bioinformatics·2025

Same author

Unraveling the molecular mechanisms underlying flat stem formation in Atractylodes lancea in response to phytoplasmas.

Physiologia plantarum·2025

Same author

Leaf physiological and endophytic microbial community characteristics and interactions of different scions grafted onto Malus sieversii.

Tree physiology·2025

Same author

Description of three new species of Spartaeini (Araneae, Salticidae, Spartaeinae) from China and Malaysia.

ZooKeys·2025

Same author

Optical transparent metamaterial emitter with multiband compatible camouflage based on femtosecond laser processing.

Nanophotonics (Berlin, Germany)·2025

Same author

Exploring deleterious non-synonymous SNPs in FUT2 gene, and implications for norovirus susceptibility and gut microbiota composition.

Scientific reports·2025

Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026

Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026

Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026

Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026

Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026

Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026

查看所有相关文章

Search research articles

相关实验视频

Updated: Jul 6, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

一个改进的二进制粒子群优化算法用于临床癌症生物标志物识别在微阵列数据中的微阵列数据.

Guicheng Yang¹, Wei Li², Weidong Xie¹

¹College of Computer Science and Engineering, Northeastern University, Shenyang, 110000, Liaoning, China.

Computer methods and programs in biomedicine

|December 29, 2023

概括

此摘要是机器生成的。

本研究引入了一种新的混合特征选择方法 (C-IFBPFE),用于分析高维微阵列数据. 该方法有效地识别了疾病诊断的关键生物标志物,提高了准确性并减少了特征数量.

关键词:

集群集成是指集群集成.嵌入式功能消除嵌入式功能消除功能选择功能选择微阵列数据的数据粒子群集优化优化粒子群集优化

更多相关视频

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

相关实验视频

Last Updated: Jul 6, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

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:

微阵列数据在特征选择方面存在挑战,原因是高维度和有限的样本.
传统的进化算法在最佳特征集中扎,在实际的时间框架内在高维空间中搜索.

研究的目的:

开发一种新的混合特征选择方法,用于微阵列数据中的生物标记物识别.
为了解决现有方法在处理高维数据和少数样本方面的局限性.

主要方法:

一种混合特征选择方法 (C-IFBPFE) 结合聚类和改进的二进制粒子群集优化 (IFBPSO) 与嵌入式特征消除策略.
利用基于相关性聚类进行初始特征选的自适应冗余特征判断方法.
纳入了改进的基于反转概率的二进制粒子群集优化 (IFBPSO) 和一种新的特征消除 (FE) 策略.

主要成果:

与现有的混合方法相比,C-IFBPFE方法在八个公共数据集中表现出更高的性能.
实现了更高的准确性,减少了特征数量,提高了灵敏度和特异性.
废弃性研究证实了每个成分的有效性,特别是特征消除策略.

结论:

拟议的C-IFBPFE方法有效地处理高维微阵列数据,样本有限.
成功地选择最小的特征子集以获得高分类准确度,并识别与疾病表型相关的强大的生物标志物.