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Related Concept Videos

Translation01:31

Translation

152.8K
Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of...
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Translation01:31

Translation

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Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Initiation of Translation02:33

Initiation of Translation

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Initiation of Translation02:33

Initiation of Translation

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Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
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Machine Translation Utilizing the Frequent-Item Set Concept.

Hanan A Hosni Mahmoud1, Hanan Abdullah Mengash2

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh P.O. Box 11671, Saudi Arabia.

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|March 6, 2021
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Summary
This summary is machine-generated.

This study introduces the Corpus-Trie (CT), a novel data structure for machine translation. The CT significantly speeds up translation by efficiently organizing phrase data, improving response times and translation quality.

Keywords:
BLEU scorebilingual corpusfrequent-item setmachine translation

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Data Structures

Background:

  • Traditional machine translation methods struggle with large corpora, leading to slow response times.
  • Sequential scanning of bilingual corpora for phrase matching is computationally inefficient.
  • Existing systems like Omega-T and Apertium have limitations in translation quality and speed with increasing corpus size.

Purpose of the Study:

  • To introduce a new data structure, the Corpus-Trie (CT), for efficient machine translation.
  • To develop algorithms that leverage the CT for faster and more accurate translations.
  • To evaluate the performance of the CT-based system against existing machine translation tools.

Main Methods:

  • A novel data structure, Corpus-Trie (CT), was developed to compactly represent bilingual parallel corpora.
  • Algorithms were designed to utilize the CT for processing translation requests.
  • Exhaustive experiments were conducted on English-Arabic and English-French language pairs.

Main Results:

  • The Corpus-Trie (CT) enables translation response times logarithmic to the number of unique phrases.
  • The CT system demonstrated improved BLEU scores with increasing corpus size for English-Arabic and English-French.
  • The CT system outperformed Omega-T and Apertium in translation quality for large corpora.

Conclusions:

  • The Corpus-Trie (CT) offers a significant advancement in machine translation efficiency and accuracy.
  • The CT is scalable and can be extended to multi-language corpora.
  • This approach provides a faster and higher-quality alternative for machine translation systems.