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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

893
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
893

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相关实验视频

Updated: Jul 2, 2025

Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

12.2K

循环RNA识别和特征解释性分析.

Mengting Niu1,2,3, Chunyu Wang4, Yaojia Chen5,6

  • 1School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China.

BMC biology
|February 26, 2024
PubMed
概括
此摘要是机器生成的。

一个新的计算框架,CircDC,准确地预测循环RNAs (circRNAs),并有助于理解它们与疾病相关的功能. 这一进步改善了现有的circRNA识别模型.

关键词:
循环RNA 是一个循环RNA.深度学习是一种深度学习.功能 功能 功能 功能.解释 解释 解释这就是 SHAP SHAP 的意思.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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相关实验视频

Last Updated: Jul 2, 2025

Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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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:

  • 循环RNAs (circRNAs) 在微RNA调节和疾病 (包括癌症) 中发挥作用.
  • 准确识别circRNAs对于功能研究至关重要,但目前的预测模型缺乏足够的准确性.
  • 需要改进用于circRNA预测和分析的计算框架.

研究的目的:

  • 开发一种新且准确的计算框架,用于从其他长非编码RNA (lncRNA) 中分类循环RNA (circRNA).
  • 增强对circRNA功能及其在生物过程和疾病中的参与的理解.

主要方法:

  • 开发了CircDC,这是一个使用四个特征编码方案的新框架.
  • 采用多层卷积神经网络和双向长期短期记忆网络,用于高阶特征表示和预测.
  • 对影响模型性能的特征进行可解释的分析.

主要成果:

  • 与现有模型相比,CircDC在circRNA预测方面表现出卓越的准确性.
  • 该框架成功识别了circRNAs,并提供了对特征重要性的见解.
  • 应用了扩展circRNA识别的计算框架.

结论:

  • CircDC是circRNA预测的有效工具,有助于更深入地了解相关的生物功能.
  • 特性重要性分析提高了模型的解释性,并揭示了重要的生物特性.
  • 开发的代码和数据是公开可用的,用于研究.