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Related Concept Videos

Improving Translational Accuracy02:07

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 Accuracy02:07

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...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

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Related Experiment Video

Updated: Jun 23, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

STORM: spatial transcriptomics optimization by resolution via matrix factorization.

Deniz Gurarslan1, Oscar Camargo2, Omer Zeyveli3

  • 1Institute of Data Science and Artificial Intelligence, Bogazici University, South Campus, Bebek, Besiktas, Istanbul 34342, Turkey.

Briefings in Bioinformatics
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

STORM, a machine learning framework, enhances spatial transcriptomics data by integrating tissue structure and gene interactions. This improves the recovery of gene-expression patterns, even with missing data, for better biological insights.

Keywords:
biologically informed regularizationcomputational pathologygene–gene interaction networkshistology-guided modelingmultimodal data integrationspatial gene–expression reconstructionspatial transcriptomicstensor decompositiontissue heterogeneitytumor microenvironment

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Last Updated: Jun 23, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) loses critical tissue architecture and spatial information.
  • Current ST platforms yield incomplete and noisy data, obscuring biological patterns.
  • Technical limitations and tissue variability hinder accurate spatial transcriptomic analysis.

Purpose of the Study:

  • Introduce STORM (spatial transcriptomics optimization by resolution via matrix factorization), a machine learning framework.
  • Improve the fidelity of spatial transcriptomics data, especially under sparse conditions.
  • Enable accurate reconstruction of spatial gene-expression patterns and preserve biological structure.

Main Methods:

  • Formulate spatial transcriptomics recovery as a low-rank tensor decomposition problem.
  • Integrate multimodal biological priors using a regularization strategy.
  • Incorporate spatial continuity, tissue morphology from histology images, and gene-gene interactions from protein-protein interaction networks.

Main Results:

  • STORM accurately reconstructs gene expression at unobserved locations.
  • The method preserves biologically meaningful spatial structures.
  • STORM outperforms state-of-the-art methods in recovering spatial gene-expression patterns in lung tissues.
  • The framework remains robust even with a majority of missing spatial measurements.

Conclusions:

  • STORM provides a reliable foundation for high-resolution spatial transcriptomic analysis.
  • The framework is effective in settings with sparse or incomplete experimental data.
  • Explicitly embedding biological structure enhances the reconstruction process for spatial transcriptomics.