Data Annotation
Data 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.
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.
06:17
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
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2025
Accurate analysis of multidimensional microscopy data requires complex workflows. This article demonstrates how to use the software Cell-ACDC. It leverages state-of-the-art AI-driven models for segmentation, tracking, cell pedigree analysis, and quantification of microscopy data. Crucially, it complements these models with an innovative framework for semi-automated correction of the models' output.
10:19
Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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Cited by 8 •
2017
Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.
FAQs
Standardized guidelines make labels more consistent across annotators, datasets, and clinical settings. They specify how features such as lesions, diagnoses, symptoms, or clinical events should be identified and recorded. Consistency gives machine-learning models clearer training signals and allows researchers to interpret differences in model performance as meaningful rather than as artifacts of inconsistent labeling.
Clinicians contribute medical judgment when labels depend on diagnoses, pathology findings, or clinically meaningful events, while trained annotators apply the defined labeling rules across large collections of data. Their work supports consistent interpretation of images, records, slides, and physiological signals. The combination helps connect technical dataset construction with medically relevant distinctions needed for reliable models.
Annotation quality directly influences training, validation, and evaluation because models learn and are judged against the labels provided in the dataset. Inaccurate or inconsistent labels can obscure whether a system recognizes clinically relevant patterns. Careful annotation therefore helps researchers measure performance, identify uncertainty, and distinguish limitations in the model from problems in the underlying data.
The annotation target depends on the source data and research question. Medical images may receive markings for lesions, pathology slides may capture relevant findings, and electronic health records or other text may be labeled for diagnoses, symptoms, or clinical events. Physiological signals can also be annotated with meaningful measurements, enabling models to learn from different forms of clinical evidence.
A typical process begins by selecting the medical data type and defining the features or events that matter, followed by applying standardized guidelines to the relevant images, records, slides, text, or signals. The resulting labels are organized into datasets used for training, validation, or evaluation. Reviewing consistency and uncertainty helps researchers judge whether the data can support dependable conclusions.
It is used when researchers need labeled evidence to develop or assess systems for diagnosis, patient monitoring, drug research, or clinical decision support. The approach is especially relevant when raw clinical information contains features that computers cannot use without structured interpretation. Well-prepared labels help connect these data sources to machine-learning investigations and potential medical applications.
Annotated datasets support safer development by enabling researchers to examine model performance, identify bias, and measure uncertainty before medical technologies are advanced. These checks are important because a model may appear effective while performing unevenly across the available data. Clear, consistent labels provide the basis for evaluating limitations and informing responsible clinical applications of artificial intelligence.