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Related Concept Videos

Translation01:31

Translation

156.3K
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...
156.3K
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
Intelligence01:27

Intelligence

8.6K
The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
8.6K
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
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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Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation.

Ruiying Zhang1,2, Yan Chen3, Wen Yue3

  • 1Department of Orthopedics, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.

Frontiers in Medicine
|January 30, 2026
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Summary

Multimodal artificial intelligence (AI) integrates diverse medical data for enhanced diagnosis and treatment planning. Combining multiple data types improves accuracy over single-source AI, advancing personalized medicine.

Keywords:
AI diagnosisclinical applicationsdata fusionmultimodal AIpersonalized therapyprecision medicine

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Area of Science:

  • Medical Artificial Intelligence
  • Data Science in Healthcare
  • Clinical Informatics

Background:

  • Multimodal artificial intelligence (AI) is revolutionizing medical practices.
  • Integrating diverse data sources like imaging, electronic health records (EHRs), and multi-omics data offers significant potential.
  • Current unimodal AI models have limitations in capturing the complexity of patient health.

Purpose of the Study:

  • To review state-of-the-art multimodal AI systems in clinical settings.
  • To highlight the benefits of integrating multiple data modalities for improved diagnostic and prognostic accuracy.
  • To address challenges and emphasize strategies for real-world clinical deployment of multimodal AI.

Main Methods:

  • Review of current multimodal AI technologies and their applications.
  • Analysis of data fusion strategies for integrating diverse medical data.
  • Exploration of model interpretability and uncertainty quantification in clinical AI.

Main Results:

  • Multimodal AI systems demonstrate superior diagnostic accuracy and prognostic prediction compared to unimodal approaches.
  • Effective data fusion strategies are crucial for successful integration of diverse data types.
  • Addressing data heterogeneity and ensuring model interpretability are key for clinical adoption.

Conclusions:

  • Multimodal AI represents a new paradigm for intelligent healthcare, enhancing clinical decision-making.
  • Continued advancements in multimodal AI will drive personalized medicine and improve patient outcomes.
  • Robust data fusion and interpretability are essential for the widespread clinical deployment of AI in medicine.