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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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相关实验视频

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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组合循环神经网络与基于鱼优化算法的DNA序列分类,用于医学应用.

Abdulaziz Alshammari1

  • 1Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Soft computing
|June 26, 2023
PubMed
概括

这项研究引入了一种使用鱼优化的新方法,用于基因表达数据中的特征选择. 该方法通过整体循环神经网络提高了病原体检测的准确性,达到99.59%的精度.

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:

  • 生物医学和临床数据收集在数据驱动的时代激增.
  • 脱氧核糖核酸 (DNA) 基因表达数据集对于通过生物标志物识别病原体至关重要.
  • 与元启发相关的特征选择 (FS) 对于管理大型基因数据集至关重要.

研究的目的:

  • 在高维度 (HD) 微阵列数据中应用鱼优化算法 (WOA) 进行特征选择.
  • 开发一个集体循环神经网络 (ERNN) 用于分类选定的基因表达数据.
  • 评估ERNN的性能与现有的先进的病原体检测方法相比.

主要方法:

  • 利用鱼优化算法 (WOA) 来从高清微阵列数据集中有效地选择特征.
  • 开发了一个集体循环神经网络 (ERNN),集成长期短期记忆 (LSTM),双向LSTM和封闭循环单元 (GRU).
  • 使用拟议的ERNN模型对所选基因特征进行分类.

主要成果:

  • WOA有效地从大型特征集中过了相关的基因,减少了计算负载.
  • ERNN模型在对基因表达数据的分类方面取得了高性能.
  • 提出的ERNN方法获得了99.59%的精度和99.59%的准确性.
关键词:
它们是DNA DNA DNA DNA.选择功能选择功能选择.基因基因 基因基因 基因基因优化算法优化算法预处理 预处理经常性的神经网络.

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结论:

  • 鱼优化算法在基因表达数据分析中的特征选择中是有效的.
  • 整体循环神经网络在使用基因组生物标志物检测病原体方面表现出卓越的性能.
  • 这种综合方法为准确和高效地分析生物医学数据提供了一个有希望的策略.