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相关概念视频

Transcription Factors02:16

Transcription Factors

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...
Transcription Factors02:16

Transcription Factors

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...
Master Transcription Regulators02:23

Master Transcription Regulators

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...
General Transcription Factors01:30

General Transcription Factors

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...
Master Transcription Regulators02:23

Master Transcription Regulators

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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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相关实验视频

Updated: May 30, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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积极学习框架利用转录组学识别疾病表型的调节者

Benjamin DeMeo1, Charlotte Nesbitt1, Samuel A Miller1

  • 1Cellarity Inc, Somerville, MA, USA.

Science (New York, N.Y.)
|October 23, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习框架使用omics数据有效地识别诱导复杂细胞表型的药物化合物. 这种方法大大提高了药物发现的成功率, 加快了新药的研发.

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相关实验视频

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科学领域:

  • 计算生物学
  • 药物发现
  • 基因组学

背景情况:

  • 现型药物查受化学空间和实验可扩展性限制.
  • 目前的计算方法往往缺乏通用性或优化能力.
  • 在药物发现中使用的基因组代理通常是启发式的,并且抵抗优化.

研究的目的:

  • 开发一个可扩展和可优化的计算框架来识别诱导复杂表型的化合物.
  • 在药物发现的积极深度学习方法中利用omics数据.
  • 提高表型药物查的效率和成功率.

主要方法:

  • 设计了一个集成omics数据的激活深度学习框架.
  • 使用一个循环中的实验室签名改进策略.
  • 在血液学发现活动中验证了算法.

主要成果:

  • 这种深度学习框架在回忆中表现出比最先进的模型更好的表现.
  • 在发现活动中实现了13至17倍的表型成功率.
  • 当与实验室循环精制相结合时, 观察到命中率增加两倍, 产生分子洞察力.

结论:

  • 开发的框架可以有效和可扩展的表型识别.
  • 这种方法有很大的潜力加速药物发现.
  • 与实验改进的整合进一步增强了击中识别,并提供了机械的理解.