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相关概念视频

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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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
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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通过大型语言模型推进生物信息学:组件,应用和观点.

Jiajia Liu1, Mengyuan Yang2, Yankai Yu3

  • 1Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, 77030, USA.

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大型语言模型 (LLM) 为生物信息学提供了强大的AI,在基因组学,转录组学和药物发现方面表现出色. 本综述详细介绍了LLM组件和应用程序,为用户和开发人员提供指导.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 大型语言模型 (LLM) 在自然语言处理 (NLP) 中表现出显著的能力,因为它们的深度学习架构和广泛的培训数据.
  • 生物信息学LLM的应用是一个新兴的领域,具有巨大的潜力,可能会超过它们在人类语言任务中的表现.
  • 了解LLM的核心组成部分和应用对于推动生物信息学研究至关重要.

研究的目的:

  • 为生物信息学领域的大型语言模型 (LLM) 提供全面的审查.
  • 涵盖基本的LLM组件,包括代币化,变压器架构,注意力机制和预训练.
  • 讨论当前的基础模型,它们在基因组学,转录组学,蛋白质组学,药物发现和单细胞分析中的应用,并提供实际指导.

主要方法:

  • 对大型语言模型及其在生物信息学中的应用现有文献的审查.
  • 详细解释关键的LLM概念,如代币化,变压器架构和注意力机制.
  • 分析当前的基础模型及其在各种生物信息学领域的下游应用.

主要成果:

  • 在解决基因组学,转录组学,蛋白组学,药物发现和单细胞分析等复杂挑战方面,LLM具有巨大的潜力.
  • 该审查概述了与生物信息学相关的LLM的关键方面,包括数据代币化和模型架构.
  • 介绍了目前可用的基础模型及其多样化的应用,强调了LLMs的实际实用性.

结论:

  • 大型语言模型是生物信息学的变革性工具,在多种生物数据类型和研究领域提供先进的解决方案.
  • 有效利用LLM需要了解其基本机制,并将其适应特定的生物信息任务.
  • 本次审查为研究人员和开发人员提供了利用LLM在生物信息学创新和优化方面的知识.