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相关概念视频

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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Improving Translational Accuracy02:07

Improving Translational Accuracy

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From DNA to Protein03:06

From DNA to Protein

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The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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相关实验视频

Updated: May 17, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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编码器转换器:使用上下文感知神经网络的多种编码器优化器.

Adibvafa Fallahpour1,2, Vincent Gureghian3,4, Guillaume J Filion5

  • 1Vector Institute for Artificial Intelligence, Toronto, ON, Canada.

Nature communications
|April 3, 2025
PubMed
概括
此摘要是机器生成的。

深度学习模型CodonTransformer通过从自然序列中学习来优化特定生物的DNA序列. 这种工具产生了类似自然的编码子使用,并最大限度地减少了改善基因表达的负面元素.

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Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
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An Integrated Approach for Microprotein Identification and Sequence Analysis
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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
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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.
  • 机器学习 机器学习

背景情况:

  • 遗传密码退化允许一个蛋白质的多个DNA序列.
  • 优化密码子使用是由于组合式爆炸而计算复杂的.
  • 自然序列提供了对进化编码子使用规则的见解.

研究的目的:

  • 开发一种深度学习模型,用于优化多个物种的DNA序列.
  • 为了生成具有自然编码子分布和最小调节元素的宿主特定DNA序列.

主要方法:

  • 开发了CodonTransformer,这是一个使用Transformer架构的多种深度学习模型.
  • 在来自164种生物的100多万个DNA-蛋白质对上训练了模型.
  • 实施了一个序列表示策略,结合了生物体,氨基酸和编码码的编码.
  • 引入了共享令牌表示和编码与对齐多重掩盖 (STREAM) 策略.

主要成果:

  • CodonTransformer 演示了上下文意识的序列生成.
  • 生成的DNA序列呈现出类似于自然的密码子分布形状.
  • 生成的序列具有最小的负 cis-regulatory 元素.
  • 该模型可以适应宿主特定的编码子优化.

结论:

  • 编码器转换器为编码器优化提供了一个有效的框架.
  • 开放访问模型和 Colab 接口促进了更广泛的应用.
  • 这种方法利用机器学习来应对合成生物学和基因工程中的挑战.