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相关概念视频

Gene Families01:57

Gene Families

8.7K
Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
8.7K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

2.8K
2.8K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.8K
Gene Therapy00:59

Gene Therapy

25.1K
Gene therapy is a technique where a gene is inserted into a person’s cells to prevent or treat a serious disease. The added gene may be a healthy version of the gene that is mutated in the patient, or it could be a different gene that inactivates or compensates for the patient’s disease-causing gene. For example, in patients with severe combined immunodeficiency (SCID) due to a mutation in the gene for the enzyme adenosine deaminase, a functioning version of the gene can be...
25.1K
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

2.4K
2.4K
Gene Conversion02:08

Gene Conversion

2.2K
2.2K

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相关实验视频

Updated: May 24, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

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作为基因分析OpenAI模型的替代品的小型开源文本嵌入模型.

Dailin Gan1, Jun Li1

  • 1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.

bioRxiv : the preprint server for biology
|March 3, 2025
PubMed
概括
此摘要是机器生成的。

开源文本嵌入模型为基因表达分析专有解决方案提供了经济有效和高效的替代方案. 这些模型在基因分类任务中显示了可比或优异的性能,而无需进行广泛的微调.

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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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相关实验视频

Last Updated: May 24, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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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:

  • 用于基因表达分析的基础变压器模型在计算上昂贵.
  • GenePT使用OpenAI的文本嵌入来进行基因信息编码.
  • 开放AI的封闭源代码模型引发了数据隐私方面的担忧.

研究的目的:

  • 调查基于开源变压器的文本嵌入模型,作为 OpenAI 服务的替代方案.
  • 评估用于基因表达数据分析的轻量级开源模型的性能.

主要方法:

  • 识别了十个小型,计算轻的变压器模型从拥抱脸.
  • 在四个不同的基因分类任务中评估模型.
  • 评估了微调对模型性能的影响.

主要成果:

  • 几种开源模型与OpenAI的性能相匹配或超过.
  • 模型大小和计算轻度是关键的选择标准.
  • 微调并不总是产生显著的性能改善.

结论:

  • 开源文本嵌入模型是基因表达分析的可行,高效和潜在的优越替代方案.
  • 这些模型减轻了与封闭源解决方案相关的数据隐私问题.
  • 为了达到高性能,往往不需要进一步的微调.