Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Short Report: An Examination of Children's Autism Traits and Their Association With Family Experience.

Autism research : official journal of the International Society for Autism Research·2026
Same author

Spatial Transcriptomics Recontextualizes the Cellular Environment of Conjunctival Melanoma.

medRxiv : the preprint server for health sciences·2026
Same author

Corneal Innervation Research at a Crossroads: A Tool-Driven Roadmap for the Future.

Investigative ophthalmology & visual science·2026
Same author

WISDOM randomized trial comparing risk-based versus annual breast cancer screening: study cohort characteristics and design.

NPJ breast cancer·2026
Same author

Utility of a Multiplex Molecular Respiratory Pathogen Panel on Clinical Management of Children in the Pediatric Emergency Department.

The Journal of molecular diagnostics : JMD·2026
Same author

RNA exosome-mediated RNA surveillance governs developmental timing in the human cerebellum.

bioRxiv : the preprint server for biology·2026

Related Experiment Videos

MeSH Up: effective MeSH text classification for improved document retrieval.

Dolf Trieschnigg1, Piotr Pezik, Vivian Lee

  • 1European Bioinformatics Institute, Hinxton, UK. trieschn@ewi.utwente.nl

Bioinformatics (Oxford, England)
|April 21, 2009
PubMed
Summary

Automated Medical Subject Headings (MeSH) annotation using a K-Nearest Neighbor (KNN) system significantly improves biomedical information retrieval (IR). This scalable approach complements manual annotations and enhances search accuracy.

Related Experiment Videos

Area of Science:

  • Biomedical Informatics
  • Information Science

Background:

  • Controlled vocabularies like Medical Subject Headings (MeSH) and Gene Ontology (GO) organize biomedical data.
  • Automating MeSH concept assignment aims to overcome limitations of manual annotation and free-text ambiguity.

Purpose of the Study:

  • To compare the performance of six automated MeSH classification systems.
  • To evaluate the reproducibility and complementarity of automated MeSH annotations compared to manual ones.
  • To assess the impact of automated MeSH annotation on information retrieval (IR).

Main Methods:

  • Comparative analysis of six MeSH classification systems: MetaMap, EAGL, language model, vector space model, K-Nearest Neighbor (KNN), and MTI.
  • Evaluation of system performance in reproducing and complementing manual MeSH annotations.
  • Assessment of IR performance using automatically annotated queries versus original textual queries.

Main Results:

  • A K-Nearest Neighbor (KNN) system demonstrated superior performance over other evaluated approaches.
  • The KNN system exhibited excellent scalability for large text datasets using the full MeSH thesaurus.
  • Automated MeSH annotation significantly improved IR performance, comparable to manual annotations.

Conclusions:

  • Automating MeSH annotation of biomedical texts enhances text-only IR.
  • The proposed automated MeSH annotation system is highly scalable.
  • Automated annotations provide IR improvements comparable to manual annotations.