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Improving Translational Accuracy

Improving Translational Accuracy

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...

Improving Translational Accuracy

Improving Translational Accuracy

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On The Translational Potential Of Atlases In Precision Oncology.

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On The Translational Potential Of Atlases In Precision Oncology.

Related Experiment Video

Production of Tissue Microarrays, Immunohistochemistry Staining and Digitalization Within the Human Protein Atlas

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On the translational potential of atlases in precision oncology.

Lucrezia Zorzi¹, Lucia Casella², Marco Dominietto¹

¹GateToBrain, Chiasso, Switzerland.

Translational Cancer Research

|December 11, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Neuroscience atlases integrate multimodal data using AI and machine learning. These advanced atlases serve as crucial tools for understanding normal anatomy and advancing precision oncology through personalized therapies.

Keywords:

Atlases brain multimodal data precision oncology radiomics

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Area of Science:

Neuroscience
Systems Biology
Neurobiology

Background:

Publicly available imaging datasets and computational power fuel neuroscience research.
Atlases are key tools providing global overviews of detailed information, organized for inference.
Atlases serve as knowledge bases from multimodal data, essential for research and clinical applications.

Purpose of the Study:

To review the translational potential of atlases in cancer studies.
To highlight how atlases integrate multiple cancer data types.
To discuss the role of atlases in inspiring predictive algorithms for personalized cancer therapy.

Main Methods:

Leveraging data multimodality in atlas development.
Utilizing machine learning (ML) and artificial intelligence (AI) for inference.

Aggregating high-resolution imaging data from diverse populations.

Main Results:

Recent atlases incorporate data multimodality and ML/AI tools.
Atlases can represent normal variation within demographic cohorts.
Multimodality in atlases enhances clinical applications, particularly in precision oncology.

Conclusions:

Atlases are evolving with advanced computational tools and diverse data.
Data multimodality in atlases is crucial for precision oncology and personalized therapy.
Atlases hold significant potential for integrating cancer data and informing predictive learning algorithms.