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Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

1.7K
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...
1.7K
Mesh Analysis with Current Sources01:10

Mesh Analysis with Current Sources

2.2K
Mesh analysis becomes simpler when analyzing circuits with current sources, whether independent or dependent. The presence of current sources reduces the number of equations required for analysis. Two cases illustrate this:
Current Source in One Mesh: The analysis process is straightforward when a current source is found in only one mesh within the circuit. Mesh currents are assigned as usual, with the mesh containing the current source excluded from the analysis. Kirchhoff's voltage law...
2.2K

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Related Experiment Video

Updated: Mar 19, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
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DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.

Shengwen Peng1, Ronghui You1, Hongning Wang2

  • 1School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.

Bioinformatics (Oxford, England)
|June 17, 2016
PubMed
Summary
This summary is machine-generated.

DeepMeSH improves biomedical text mining by using deep semantic information for Medical Subject Headings (MeSH) indexing. This novel approach enhances accuracy in assigning MeSH terms to citations, outperforming existing methods.

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Information Retrieval

Background:

  • Medical Subject Headings (MeSH) indexing is vital for biomedical text mining and information retrieval.
  • Current methods like MTI and MeSHLabeler use bag-of-words, limiting capture of semantic and context-dependent information.
  • Large-scale MeSH indexing faces challenges on both citation and MeSH sides.

Purpose of the Study:

  • To propose DeepMeSH, a novel method for large-scale MeSH indexing incorporating deep semantic information.
  • To address limitations of existing methods in capturing semantic nuances for citation indexing.
  • To improve the accuracy and efficiency of assigning MeSH terms to biomedical literature.

Main Methods:

  • Developed DeepMeSH, a system leveraging deep semantic representations for MeSH indexing.
  • Introduced D2V-TFIDF, a new deep semantic representation concatenating sparse and dense features for citations.
  • Utilized a 'learning to rank' framework, integrating evidence from the new semantic representation for MeSH term assignment.

Main Results:

  • DeepMeSH achieved a Micro F-measure of 0.6323 on BioASQ3 challenge data.
  • This represents a 2% improvement over MeSHLabeler and a 12% improvement over MTI.
  • The method demonstrated superior performance on a dataset of 6000 citations.

Conclusions:

  • DeepMeSH effectively incorporates deep semantic information for enhanced MeSH indexing.
  • The proposed D2V-TFIDF representation and 'learning to rank' framework significantly improve indexing accuracy.
  • DeepMeSH offers a more robust solution for large-scale biomedical text mining and information retrieval.