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Related Concept Videos

Translation01:31

Translation

156.4K
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...
156.4K
Translation01:31

Translation

17.8K
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...
17.8K
Initiation of Translation02:33

Initiation of Translation

39.0K
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...
39.0K
Termination of Translation01:44

Termination of Translation

27.7K
The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
27.7K
Machines01:19

Machines

579
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
579
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.9K
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...
14.9K

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Related Experiment Video

Updated: Feb 3, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Neural Machine Translation with Deep Attention.

Biao Zhang, Deyi Xiong, Jinsong Su

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 19, 2018
    PubMed
    Summary

    This study introduces DeepAtt, a novel deep attention model for neural machine translation. DeepAtt enhances translation accuracy by optimizing the attention mechanism, leading to more faithful translations.

    Area of Science:

    • Natural Language Processing
    • Deep Learning
    • Machine Translation

    Background:

    • Deep neural models enhance capacity for complex tasks like machine translation.
    • Existing deep neural machine translation focuses on encoder-decoder, neglecting the attention mechanism.
    • Attention mechanisms are crucial for cross-lingual correspondence, especially in deep networks.

    Purpose of the Study:

    • To propose a deep attention model (DeepAtt) for improved neural machine translation.
    • To enhance the attention mechanism's role in deep neural networks.
    • To improve the accuracy and faithfulness of machine translations.

    Main Methods:

    • Developed DeepAtt, a deep attention model.
    • Utilized low-level attention information to guide information flow from encoder layers.

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  • Incorporated multiple attention layers within the model architecture.

    • Main Results:

    • DeepAtt achieved competitive performance on NIST Chinese-English, WMT English-German, and WMT English-French translation tasks.
    • Increasing attention layers in DeepAtt led to more accurate attention weights.
    • The model significantly improved the translation of important context words, enhancing faithfulness.

    Conclusions:

    • DeepAtt offers a significant advancement in deep neural machine translation by focusing on the attention mechanism.
    • The proposed model demonstrates superior performance and faithfulness compared to state-of-the-art methods.
    • DeepAtt's architecture effectively leverages attention for improved cross-lingual understanding.