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Next-generation Sequencing03:00

Next-generation Sequencing

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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
Although all next-generation methods use different technologies, they all share a set of standard features....
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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
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.
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相关实验视频

Updated: Jun 25, 2025

Automated Robotic Liquid Handling Assembly of Modular DNA Devices
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Automated Robotic Liquid Handling Assembly of Modular DNA Devices

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一种基于知识的自我监督的方法,用于分子生成.

Maotao Liu, Yifan Yang, Qun Liu

    IEEE/ACM transactions on computational biology and bioinformatics
    |May 28, 2024
    PubMed
    概括

    本研究介绍了一种基于知识的自我监督模型,用于分子表示学习 (KSMRL),以改善分子学习. 通过结合空间和亚结构信息,KSMRL增强了分子表示,在生成和属性优化任务中超越现有方法.

    科学领域:

    • 计算化学计算化学
    • 机器学习 机器学习
    • 药物发现 药物发现 药物发现

    背景情况:

    • 图形神经网络 (GNN) 显示出分子学习的前景,但往往忽视空间结构和亚结构属性.
    • 现有的GNN方法可能会通过忽视关键的分子几何学和功能组信息来降低下游任务的性能.

    研究的目的:

    • 开发一种新的模型,即知识驱动的分子表示学习自我监督模型 (KSMRL),以解决当前基于GNN的分子表示学习的局限性.
    • 通过整合空间信息和亚结构属性来增强分子表示,以提高下游任务性能.

    主要方法:

    • KSMRL使用两个路径:一个空间信息 (SI) 路径来保持分子几何学和一个子图约束 (SC) 路径来保持基底结构特征.
    • 该模型整合了原子层面和亚结构层面的信息,以实现全面的分子表示.

    主要成果:

    • KSMRL生成有区别的分子表示,在多个数据集中验证.
    • 当与自回流 (AF) 或离散流 (DF) 模型相结合时,KSMRL增强的分子生成超过了最先进的基线.
    • 财产优化实验和关于药物向相互作用 (DTI) 的案例研究证明了KSMRL的有效性.

    结论:

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  • KSMRL有效地捕获空间和亚结构信息,从而获得更优质的分子表示.
  • 拟议的模型在分子生成,属性优化和预测药物向相互作用方面提供了显著的改进.
  • 在将图形神经网络应用于复杂的分子学习挑战方面,KSMRL代表了一个关键的进步.