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

Leaky Scanning02:28

Leaky Scanning

5.1K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.1K

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

High mobility group box 1 (HMGB1) levels in the placenta and in serum in preeclampsia.

American journal of reproductive immunology (New York, N.Y. : 1989)·2011
Same author

Destabilization of coxsackievirus b3 genome integrated with enhanced green fluorescent protein gene.

Intervirology·2011
Same author

[Clinicopathological features of primary splenic histiocytic sarcoma: a case report and literature review].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2011
Same author

[Comparison of treatment with micro endoscopic discectomy and posterior lumbar interbody fusion using single and double B-Twin expandable spinal spacer].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2011
Same author

Virtual transplantation in designing a facial prosthesis for extensive maxillofacial defects that cross the facial midline using computer-assisted technology.

The International journal of prosthodontics·2011
Same author

Total synthesis of phorboxazole A via de novo oxazole formation: convergent total synthesis.

Journal of the American Chemical Society·2010

相关实验视频

Updated: Jun 10, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
00:06

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

13.6K

数据增强的机器学习评分功能用于虚拟选YTHDF1 m6A读者蛋白质.

Muhammad Junaid1, Bo Wang2, Wenjin Li2

  • 1Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, China; College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen, 518060, China.

Computers in biology and medicine
|October 15, 2024
PubMed
概括

机器学习模型预测YTHDF1抑制剂用于癌症治疗. 具有蛋白质连接体扩展连接指纹 (ANN-PLEC) 的人工神经网络在基于结构的药物发现中表现出卓越的性能.

关键词:
数据增强数据增强机器学习的评分功能是机器学习的评分功能.分子对接是分子对接.基于结构的虚拟选.针对特定目标的评分功能.在 YTHDF1 的位置上.

更多相关视频

A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants
06:35

A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants

Published on: October 10, 2022

1.9K
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

相关实验视频

Last Updated: Jun 10, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
00:06

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

13.6K
A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants
06:35

A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants

Published on: October 10, 2022

1.9K
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

科学领域:

  • 计算化学和化学信息学
  • 药物发现和药物化学
  • 在生物信息学中的机器学习.

背景情况:

  • 机器学习加速了药物发现,特别是基于结构和连接体的方法.
  • 有限的抑制剂数据需要数据增强以提高模型性能.
  • YTHDF1是一个有前途的癌症治疗标,但传统的虚拟查方法与其独特的结合性特征作斗争.

研究的目的:

  • 开发用于基于结构的药物发现的预测机器学习模型.
  • 创建特定于YTHDF1的机器学习评分函数 (MLSFs),以改进基于结构的虚拟选 (SBVS).
  • 由于数据有限和蛋白质结合复杂性,解决识别强效YTHDF1抑制剂的挑战.

主要方法:

  • 训练有素的预测模型使用传统的机器学习算法对目标和连接体动态感知数据集进行训练.
  • 使用数据增强开发了YTHDF1特定的MLSF,包括多重连接体和蛋白质构造.
  • 用四个算法训练了64个MLSF,并在十个测试集上评估了它们的预测和排名能力.

主要成果:

  • 带有蛋白质连接体扩展连接指纹 (ANN-PLEC) 的人工神经网络表现出卓越的性能.
  • 安恩-普莱克在0.87.8的精度回忆曲线 (PR-AUC) 下实现了高面积.
  • 开发的MLSF对药物发现有前途,针对具有有限活性分子数据的蛋白质.

结论:

  • ANN-PLEC模型为基于结构的药物发现提供了一种可行的方法,特别是对于像YTHDF1.1这样具有挑战性的目标.
  • 这种方法通过提供针对目标蛋白调整的特定评分功能来提高SBVS的有效性.
  • ANN-PLEC评分功能在GitHub上公开提供,以促进进一步的研究.