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関連する概念動画

The Evidence for Evolution02:55

The Evidence for Evolution

48.4K
Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
48.4K
Convergent Evolution01:54

Convergent Evolution

33.1K
Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
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Ranks01:02

Ranks

510
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
510
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.5K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.5K
Free Energy01:21

Free Energy

52.3K
Free energy—abbreviated as G for the scientist Gibbs who discovered it—is a measurement of useful energy that can be extracted from a reaction to do work. It is the energy in a chemical reaction that is available after entropy is accounted for. Reactions that take in energy are considered endergonic and reactions that release energy are exergonic. Plants carry out endergonic reactions by taking in sunlight and carbon dioxide to produce glucose and oxygen. Animals, in turn, break...
52.3K
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

778
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
778

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Updated: Feb 13, 2026

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
09:01

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Published on: March 16, 2011

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エネルギーランクアラインメントを備えた効率的で短距離の誘導進化.

Sebastian Ibarraran, Shriram Chennakesavalu, Frank Hu

    bioRxiv : the preprint server for biology
    |February 12, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    この研究は,適応したタンパク質言語モデルを使用して,タンパク質工学の新しい方法を導入しています. このアプローチは,より少ない実験データで高フィットネスタンパク質配列を効率的に特定し,誘導進化戦略を改善します.

    さらに関連する動画

    In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity
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    Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening
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    Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
    09:01

    Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

    Published on: March 16, 2011

    31.2K
    In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity
    09:16

    In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity

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    Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening
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    科学分野:

    • バイオケミストリー バイオケミストリー
    • コンピュータ生物学 コンピュータ生物学
    • プロテイン工学は,タンパク質の

    背景:

    • 誘導進化はタンパク質工学にとって極めて重要ですが,実験データの高コストによって制限されています.
    • シーケンス選択のための現在の機械学習方法は,稀少なデータによって制限され,モデルの複雑性を制限しています.

    研究 の 目的:

    • 大規模なタンパク質言語モデルを適応することによって,タンパク質工学のより効率的な方法を開発する.
    • 複雑なフィットネス環境をナビゲートするために,天然のタンパク質配列分布を活用する.

    主な方法:

    • 実験データを用いて,大規模にプリートレーニングされたタンパク質言語モデルを適応した.
    • 統計物理に基礎を置く研修後のアルゴリズムを使用した.
    • 多様で高フィットネスな配列を生成するために定量的な実験ランキングを活用しました.

    主要な成果:

    • タンパク質工学で顕著な効率の向上を達成しました.
    • 競合する方法と比較して,実験データポイントを少なくする必要がありました.
    • 適応されたモデルは,高適合性配列の生体物理学的特性についての洞察を提供した.

    結論:

    • タンパク質言語モデルの適応は,効率的なタンパク質工学のための強力な代替案を提供します.
    • この方法は,高次元フィットネス・ランドスケープを効果的にナビゲートします.
    • 適応されたモデルは解釈可能であり,タンパク質の性質の理解を深める.