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相关概念视频

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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Master Transcription Regulators02:23

Master Transcription Regulators

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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Transcription Factors02:16

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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General Transcription Factors01:30

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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对大型语言模型进行基准测试,以识别转录因子调节相互作用.

Lake Noel1, Yi-Wen Hsiao1, Yimeng He1,2

  • 1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.

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概括

大型语言模型 (LLM) 对识别转录因子 (TF) 目标基因相互作用显示出希望. 克劳德3.5索内特和GPT-4o表现出强的表现,迅速的工程显著提高了监管生物学研究的准确性.

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科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 转录因子 (TF) 和它们的基因协调基因表达,影响生物过程和疾病.
  • 现有的识别TF-目标相互作用的方法通常需要专门的计算专业知识.
  • 大型语言模型 (LLM) 为查询这些复杂的监管关系提供了更容易获得的方法.

研究的目的:

  • 为了比较著名的LLM在识别人类TF-目标相互作用方面的表现.
  • 评估快速工程和模型参数对TF-target预测的LLM精度的影响.
  • 评估不同类型的监管互动 (双向,模两可,自我监管,单向) 的LLM能力.

主要方法:

  • 使用文献策划 (8432次互动) 和实验衍生 (5148次互动) 人类TF目标数据集进行四个LLM (Claude 3.5 Sonnet,Gemini 1.0 Pro,GPT-4o,Llama3 8b) 的基准测试.
  • 分析基于单回合和多回合查询以及零温度设置的性能.
  • 评估四个监管类别的准确性:双向,模两可,自我监管和单向.

主要成果:

  • 克劳德3.5索内特和GPT-4o在单回合查询中表现出竞争力.
  • 多个转促使一些模型的准确性显著提高,特别是克劳德3.5索内特在自我调节对 (+32.6%) 上.
  • 除了未知的调节类型,提高了准确性,单向调节达到近70%的平衡准确性.
  • 克劳德3.5索内特在各种条件下与实验推导的相互作用中始终优于其他模型.

结论:

  • 在监管生物学中,LLM聊天机器人,特别是Claude 3.5 Sonnet,显示了TF-target交互识别的巨大潜力.
  • 快速工程和参数调整对于优化LLM在这个领域的性能至关重要.
  • 这些发现建立了一个基准测试框架,并强调了一般目的LLM对于缺乏专业计算专业知识的研究人员的实用性.