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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Modern Molecular Taxonomy01:29

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Maxam-Gilbert Sequencing01:05

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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.
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Updated: Aug 13, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Beyond sequence: Structure-based machine learning.

Janani Durairaj1,2, Dick de Ridder2, Aalt D J van Dijk2

  • 1Biozentrum, University of Basel, Basel, Switzerland.

Computational and Structural Biotechnology Journal
|January 20, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning is now using protein structures, not just sequences, for analysis. This review explores structure-based machine learning methods and their applications in protein biology.

Keywords:
Deep learningMachine learningProtein structures

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Machine learning in protein science

Background:

  • Recent advances in protein structure prediction herald a new era in structural bioinformatics.
  • The increasing availability of protein structure data rivals that of sequence data.
  • Machine learning in protein bioinformatics is shifting from sequence-based to structure-based approaches.

Purpose of the Study:

  • To review various structure-based machine learning approaches in protein biology.
  • To discuss how protein structures are utilized as input for machine learning models.
  • To highlight applications, challenges, and opportunities in this field.

Main Methods:

  • Literature review of structure-based machine learning methods.
  • Analysis of how structural information is incorporated into machine learning models.
  • Categorization of applications across different scopes (single protein family to all proteins).

Main Results:

  • Machine learning methods increasingly leverage rich structural information beyond sequence data.
  • Approaches vary in scope, from specific protein families to comprehensive analyses across all proteins.
  • Diverse applications in protein biology are emerging from structure-based machine learning.

Conclusions:

  • Structure-based machine learning represents a significant and growing area in protein bioinformatics.
  • Further development is needed to address current challenges and capitalize on opportunities.
  • This field promises to enhance our understanding of protein biology through advanced computational methods.