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相关概念视频

Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Factors Affecting Drug Biotransformation: Physicochemical and Chemical Properties of Drugs01:21

Factors Affecting Drug Biotransformation: Physicochemical and Chemical Properties of Drugs

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A drug's physicochemical properties fundamentally influence its metabolism. For instance, a drug's molecular size and shape critically determine its interaction with enzymes and transporters — larger drugs may face difficulty reaching enzyme active sites, altering their metabolic pathways. The pKa of a drug, which establishes its ionization state, can impact its solubility and absorption, thereby influencing metabolism.
The drug's acidity or basicity is essential in...
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Types of Chemical Bonds02:37

Types of Chemical Bonds

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Chemical bonding theories were pioneered by American chemist Gilbert N. Lewis. He developed a model called the Lewis model to explain the type and formation of different bonds. Chemical bonding is central to chemistry; it explains how atoms or ions bond together to form molecules. It explains why some bonds are strong and others are weak, or why one carbon bonds with two oxygens and not three; why water is H2O and not H4O. 
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Components of Language01:24

Components of Language

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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.
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Language Development01:22

Language Development

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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...
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Language and Cognition01:27

Language and Cognition

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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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XRepDDA:一种可解释的药物疾病协会预测框架,利用预先训练的化学语言模型.

Chenyi Zhang1, Yun Zuo1, Qiao Ning1

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, Wuxi 214122, China.

Journal of chemical information and modeling
|January 30, 2026
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这项研究介绍了XRepDDA,这是一个用于预测药物-疾病关联 (DDA) 的新型框架,通过将先进的药物和疾病表示与深度度度度学习相结合. XRepDDA显著提高了药物重新定位的预测准确性和稳定性.

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科学领域:

  • 计算机化药物发现.
  • 药理学 药理学是指药理学的学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 药物重新定位通过为现有药物找到新的用途来加速治疗发展.
  • 准确预测药物疾病关联 (DDAs) 是至关重要的,但由于药物表现不佳,疾病语义有限和数据不平衡而受到挑战.
  • 由于这些局限性,现有的计算方法在预测准确度和概括方面扎.

研究的目的:

  • 开发一个创新的框架,XRepDDA,用于增强DDA预测.
  • 提高计算药物重新定位策略的准确性和稳定性.
  • 解决药物表示,疾病语义和DDA预测数据不平衡方面的局限性.

主要方法:

  • 使用SMI-TED用于药物嵌入的综合多式特征表示和用于疾病表示的层次语义图 (MeSH本体学).
  • 采用深度度度度学习,并改进了ModernNCA架构,用于歧视性嵌入空间学习.
  • 利用AllKNN自适应的低抽样来缓解类不平衡,以及多层次的可解释性框架 (SHAP,注意力,分子扰动) 进行解释.

主要成果:

  • 在基准数据集上,XRepDDA的表现明显优于基线模型,AUC和AUPR值达到0.9990和0.9991.
  • 通过分子对接的in silico验证支持阿尔茨海默病和胃瘤的预测可靠性.
  • 可解释性框架证明了预测的化学解释性和生物可信性.

结论:

  • XRepDDA提供了一个强大而准确的计算框架,用于药物疾病关联预测.
  • 集成高级表示和深度度度学习提高了药物重新定位的效率.
  • 开发的可解释性方法为预测机制提供了关键的见解,支持临床翻译.