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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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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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Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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相关实验视频

Updated: Jun 16, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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SpanSeq:基于相似性的序列数据分割方法,用于改进深度学习项目的开发和评估.

Alfred Ferrer Florensa1, Jose Juan Almagro Armenteros2, Henrik Nielsen3

  • 1Research Group for Genomic Epidemiology, DTU National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark.

NAR genomics and bioinformatics
|August 19, 2024
PubMed
概括
此摘要是机器生成的。

为了深度学习,随机分割生物数据可能会导致模型评估不准确. SpanSeq是一种新的数据库分区方法,可以防止数据泄露,确保可靠的计算生物学概括.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 机器学习 机器学习
  • 生物信息学是一种生物信息学.

背景情况:

  • 深度学习模型在计算生物学中越来越多地使用,但可以学习杂的数据模式.
  • 标准的随机数据分割用于模型概括评估,由于样本相似性,通常是不可靠的.

研究的目的:

  • 介绍SpanSeq,一种用于机器学习的新型数据库分区方法.
  • 解决生物序列数据集 (基因,蛋白质,基因组) 中数据泄露的问题.
  • 评估数据分割策略对模型评估和开发的影响.

主要方法:

  • 开发了SpanSeq,这是一个可扩展的生物序列数据库分区方法.
  • 复制了两种最先进的生物信息学模型的发展.
  • 分析了开发和测试集之间无限制相似性的影响.

主要成果:

  • 证实随机数据分割导致对模型概括的可疑评估.
  • 证明了数据分割策略也会影响模型开发.
  • SpanSeq有效地防止了培训和测试集之间的数据泄露.

结论:

  • 在计算生物学中,SpanSeq为可靠的模型评估提供了一个强大的解决方案.
  • 仔细的数据分区对于准确评估和开发深度学习模型至关重要.
  • 该方法适用于各种生物序列类型和可扩展.