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Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
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Predictive Immune Modeling of Solid Tumors
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编码DNA图书馆的组成深度概率模型.

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此摘要是机器生成的。

用DNA编码的库 (DEL) 产生复杂的数据. 一个新的深度学习模型,DEL-Compose,通过将分子分解成构建块来分析这些数据,改善药物发现洞察力.

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用
  • 生物信息学是一种生物信息学.

背景情况:

  • DNA编码图书馆 (DEL) 是用于选小分子的强大工具.
  • 德尔实验产生复杂的数据,可以掩盖重要的信号.
  • 需要机器学习来从复杂的DEL数据中提取见解.

研究的目的:

  • 介绍DEL-Compose,这是一个用于DEL数据分析的深度概率模型.
  • 改进使用共变因子对DEL计数数据的观察模型.
  • 提高DEL数据分析的可解释性和可靠性.

主要方法:

  • 将分子表示分解为单合成,异合成和三合成的构建块.
  • 使用层次结构模拟嵌入式synthons之间的潜在反应.
  • 在观察模型中集成协变因子,以考虑数据噪声.

主要成果:

  • 在公开基准数据集 (CA-IX和HRP) 上,DEL-Compose表现强.
  • 该模型成功丰富了正确的药理,表明有效的结合剂识别.
  • 本质的可解释结构为DEL数据提供了有价值的见解.

结论:

  • DEL-Compose提供了一个强大的计算工具,用于分析复杂的DNA编码图书馆数据.
  • 该模型的方法增强了强有力的结合剂的发现,并提供了可解释的结果.
  • 这项工作推动了机器学习在基于DEL的药物发现中的应用.