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

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.6K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
6.6K

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

PeptideSGCL: Structure-Enhanced Graph-Transformer Encoding and Dual-Level Contrastive Learning for Peptide Property Prediction.

ACS synthetic biology·2026
Same author

CMA-Nano: A DNA Methylation Detection Method for Nanopore Sequencing Data Based on a Cross-Modal Attention Mechanism.

ACS omega·2026
Same author

An Explainable Deep Learning Framework Integrating DNA Sequence and Transcription Initiation Signals for Gene Expression Prediction.

ACS synthetic biology·2026
Same author

LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides.

ACS synthetic biology·2026
Same author

Activated carbon characteristics and interaction mechanisms affect organic micropollutant removal from secondary effluent through stand-alone adsorption and in combination with ozonation.

Journal of hazardous materials·2026
Same author

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

Journal of chemical information and modeling·2026

相关实验视频

Updated: Jul 28, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.1K

CACPP:一个基于学习的对比的语网络,以仅基于序列识别抗癌.

Xuetong Yang1,2, Junru Jin1,2, Ruheng Wang1,2

  • 1School of Software, Shandong University, Jinan 250101, China.

Journal of chemical information and modeling
|May 30, 2023
PubMed
概括

这项研究介绍了CACPP,这是一种用于准确预测抗癌 (ACP) 的深度学习模型. CACPP显著优于现有方法,为识别潜在的癌症疗法提供了更快,更有效的方法.

更多相关视频

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology
08:27

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

Published on: March 24, 2015

14.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

150

相关实验视频

Last Updated: Jul 28, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.1K
Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology
08:27

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

Published on: March 24, 2015

14.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

150

科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 在瘤学瘤学.

背景情况:

  • 抗癌 (ACP) 由于其有效性和安全性,在癌症治疗中表现有前途.
  • 试验性识别ACP是昂贵的和耗时的.
  • 传统的机器学习方法用于ACP预测,由于依赖于手工制作的功能,其性能较低.

研究的目的:

  • 开发一种新的深度学习框架,CACPP,用于准确预测抗癌.
  • 通过利用先进的深度学习技术,改进现有的非洲和非洲国家和地区预测方法.

主要方法:

  • 提出了CACPP,这是一个集结卷积神经网络 (CNN) 和对比学习的深度学习框架.
  • 利用TextCNN直接从序列中提取高隐性特征.
  • 采用对比学习模块来增强特征表示的区分能力.

主要成果:

  • 在基准数据集上,CACPP与最先进的方法相比,表现优越.
  • 功能可视化证实了该模型强大的分类能力.
  • 分析探讨了用经过验证的负样本对数据集构建的影响和性能.

结论:

  • CACPP为抗癌预测提供了一个高度准确和高效的方法.
  • 深度学习方法克服了传统功能工程的局限性.
  • 这一框架推动了用于癌症治疗的新型治疗性的鉴定.