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相关概念视频

Next-generation Sequencing03:00

Next-generation Sequencing

86.9K
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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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

3.9K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
3.9K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

10.9K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
10.9K
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.
18.8K
Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

3.4K
Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
3.4K
Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

5.2K
In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...
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相关实验视频

Updated: May 24, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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具有序列对序列 (Seq2Seq) 模型的自适应多步预测.

Joseph Kelley, Martin Hagan

    IEEE transactions on neural networks and learning systems
    |March 3, 2025
    PubMed
    概括

    序列对序列 (Seq2Seq) 模型作为固定权重的适应性预测器,适应变化的数据而没有权重更新. 它在循环神经网络解码器中的嵌入式学习算法使这种适应成为可能.

    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 时间序列分析时间序列分析

    背景情况:

    • 传统的预测模型通常需要频繁的重新培训,以适应不断变化的数据模式.
    • 适应性预测对于准确预测具有时间变化动态的系统至关重要.

    研究的目的:

    • 证明序列对序列 (Seq2Seq) 模型作为自适应的多步预测器.
    • 为了研究使Seq2Seq模型能够适应时间变化的行为而不需要重量更新的机制.

    主要方法:

    • 使用固定权重的自适应方法,其中学习嵌入在循环神经网络 (RNN) 解码器中.
    • 使用模拟和实验数据集测试Seq2Seq模型的适应能力.

    主要成果:

    • Seq2Seq模型展示了自适应的多步预测能力.
    • 该研究确定了一种特定的内部机制,该机制负责模型适应时间变化的行为.

    结论:

    • Seq2Seq模型为适应性预测提供了一种新的方法,减少了对恒定权重和偏差调整的需求.
    • 在RNN解码器中嵌入式学习算法是模型处理动态系统的能力的关键.

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    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
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    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

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    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
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    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

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