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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

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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Related Experiment Video

Updated: Jun 23, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.

Yuran Jia¹, Junliang Liu¹, Li Chen²

¹Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150040, China.

Briefings in Bioinformatics

|December 25, 2023

Summary

This summary is machine-generated.

THItoGene predicts spatial gene expression from pathology images using a novel AI approach. This method offers a cost-effective alternative to spatial transcriptomics, accurately revealing gene regulation insights from histology.

Keywords:

capsule network histopathological images spatial transcriptomics transformer

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Area of Science:

Computational Biology
Genomics
Pathology

Background:

Spatial transcriptomics provides insights into cellular regulation but is expensive.
Current AI methods for predicting spatial gene expression from histology lack deep information extraction capabilities.

Purpose of the Study:

To develop an affordable and effective method for predicting spatial gene expression from histological images.
To explore the relationship between high-resolution pathology image phenotypes and gene expression regulation.

Main Methods:

Developed THItoGene, a hybrid neural network combining dynamic convolutional and capsule networks.
Utilized deep learning to adaptively sense molecular signals within histological images.
Evaluated performance on human breast cancer and cutaneous squamous cell carcinoma datasets.

Main Results:

THItoGene demonstrated superior performance in spatial gene expression prediction compared to existing methods.
The model successfully deciphered spatial context and enrichment signals within specific tissue regions.
Validated on diverse human cancer datasets, showcasing robust predictive power.

Conclusions:

THItoGene offers a cost-effective and accurate solution for spatial gene expression prediction from histology.
The AI tool can reveal complex gene regulation patterns and spatial tissue information.
This approach enhances the utility of pathological images for genomic research.