Digital Slide Annotation
Digital Slide Annotation - Related Videos
08:09
Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Cited by 8 •
2012
Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.
03:36
Genome Annotation and Assembly
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2021
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
01:18
Design Example: Designing Water Slide
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2025
When designing a water slide, controlling the speed of water flow is crucial for rider safety while maintaining an exciting experience. As water flows down the slide, gravity causes it to accelerate, with its speed at the bottom depending on the height from which it starts. The higher the slide, the more potential energy the water has at the top, which is converted into kinetic energy as it descends, increasing its speed. Bernoulli's principle determines the water's velocity along the slide.
06:14
In vitro Biofilm Formation in an 8-well Chamber Slide
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Cited by 62 •
2011
This article describes the procedure for the formation and visualization of a bacterial biofilm grown within an 8-well chamber...
06:16
mirMachine: A One-Stop Shop for Plant miRNA Annotation
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Cited by 11 •
2021
Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.
FAQs
Boundary placement and label consistency determine which visual regions or structures become analyzable data. Outlining a tumor region can support area-based measurement, while labeling cells or tissue structures preserves finer-grained information for comparison. Consistent decisions across slides make datasets easier to review and can improve their usefulness for computational model development.
Standardized labels create a shared vocabulary for selected findings, reducing ambiguity when multiple slides or contributors are compared. They help organize observations into structured datasets that can be reviewed and reused across analyses. In cancer studies, this consistency supports classification, biomarker assessment, and spatial analysis by linking comparable visual features to the same analytical categories.
Linking morphology to structured digital data turns visual observations into information that can be measured, compared, and examined computationally. This connection allows researchers to relate tumor appearance, cellular findings, or tissue organization to specific labels rather than relying only on unstructured viewing. The resulting records support reproducibility and provide training material for image-analysis methods.
A basic workflow begins with a high-resolution scanned pathology image, followed by selection of a relevant region or feature. The researcher then outlines regions, identifies cells or tissue structures, and assigns labels that organize the observations. The completed annotations can be reviewed, measured, or assembled into datasets for computational analysis and model training.
It can support studies of tumor classification, biomarker assessment, spatial analysis, and treatment-related changes. The appropriate annotation target depends on the question: broad tumor regions may help classify tissue, while cell or structure labels can preserve more localized information. These datasets also enable teams to examine pathology images systematically across research projects.
Annotations give collaborators a shared, reviewable record of which image features were selected and how they were labeled. That record makes visual judgments easier to communicate, compare, and revisit than an image viewed without structured markings. In translational cancer research, this supports reproducibility while helping pathology and computational researchers work from the same digital evidence.