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相关概念视频

Next-generation Sequencing03:00

Next-generation Sequencing

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
Although all next-generation methods use different technologies, they all share a set of standard features.
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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.
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
Components of Language01:24

Components of Language

Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs. “eh”). Phonemes combine to...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Language and Cognition01:27

Language and Cognition

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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Updated: Jun 18, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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为下一代单细胞分析扩展大型语言模型.

Syed Asad Rizvi1, Daniel Levine2, Aakash Patel2

  • 1Yale University, Google Research.

bioRxiv : the preprint server for biology
|November 24, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了C2S-Scale,这是一个大型语言模型 (LLM),在超过10亿个转录和生物文本数据标记上进行训练. 这种先进的模型增强了单细胞RNA测序分析,并通过整合各种数据类型来指导生物发现.

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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.
  • 人工智能的人工智能

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 显示了细胞多样性,但目前的模型缺乏可扩展性和文本集成性.
  • 现有的单细胞基础模型 (scFMs) 难以处理各种任务,并将转录组数据与生物文本结合起来.
  • 该Cell2Sentence (C2S) 框架将scRNA-seq配置文件表示为文本,为整合各种数据提供了基础.

研究的目的:

  • 通过整合转录和文本数据,开发一个可扩展和多用途的单细胞分析基础模型.
  • 通过对大量生物数据进行培训,增强LLM在生物研究方面的能力.
  • 证明C2S-Scale模型在预测生物反应和指导实验发现方面的实用性.

主要方法:

  • 通过使用C2S框架对超过10亿个转录数据,生物文本和元数据令牌进行了27亿参数的LLM培训.
  • 使用强化学习进行有针对性的微调,以提高特定生物任务的性能.
  • 利用双语境虚拟屏幕来提名药物候选人,以选择语境生物效应.

主要成果:

  • 在各种下游任务中,C2S-Scale在预测和生成能力方面取得了持续的改进.
  • 该模型在扰乱响应预测,自然语言解释和生物推理方面取得了强的表现.
  • 一个虚拟屏幕提名了silmitasertib (CX-4945) 进行情境选择性抗原呈现上调,这在人类细胞模型中经过实验验证.
  • C2S-Scale在前所未有的规模上有效地整合了转录和文本数据,超过了专门的scFM和一般的LLM.

结论:

  • C2S-Scale代表了单细胞分析的重大进步,为下一代研究统一了各种数据类型.
  • 该模型能够指导环境条件下的生物发现,这为药物开发和生物理解开辟了新的途径.
  • C2S-Scale提供了一个强大的平台来创建"虚拟细胞",并推动计算生物学的界限.