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Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
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A chemical formula presents information about the proportions of atoms constituting a particular chemical compound or molecule, mainly using symbols of elements and numbers. At times other symbols, such as dashes, parentheses, brackets, commas, plus, and minus signs, are also used. A chemical formula can be one of three types – molecular, empirical, and structural.
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Related Experiment Video

Updated: Jan 24, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Knowledge-guided convolutional networks for chemical-disease relation extraction.

Huiwei Zhou1, Chengkun Lang2, Zhuang Liu2

  • 1School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China. zhouhuiwei@dlut.edu.cn.

BMC Bioinformatics
|May 23, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Knowledge-guided Convolutional Networks (KCN) for extracting chemical-disease relations (CDR) using prior knowledge from biomedical databases. The KCN model significantly improves CDR extraction performance by integrating knowledge and contextual information.

Keywords:
Attention mechanismCDR extractionContext featuresGating unitsKnowledge representations

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Automatic extraction of chemical-disease relations (CDR) is crucial for disease treatment and drug development.
  • Biomedical knowledge bases (KBs) offer valuable prior knowledge for CDR extraction.
  • Effectively utilizing prior knowledge remains a key challenge in CDR extraction.

Purpose of the Study:

  • To propose a novel model, Knowledge-guided Convolutional Networks (KCN), for leveraging prior knowledge in CDR extraction.
  • To enhance the accuracy of automatic chemical-disease relation identification.

Main Methods:

  • Developed KCN model integrating entity and relation embeddings from KBs.
  • Employed gated convolutions controlled by entity embeddings to propagate context features.
  • Utilized shared attention pooling with relation embeddings to capture weighted context features.

Main Results:

  • The KCN model achieved a 71.28% F1-score on the BioCreative V CDR dataset.
  • KCN outperformed most existing state-of-the-art systems for CDR extraction.
  • Demonstrated effective integration of prior knowledge and contextual information.

Conclusions:

  • The proposed KCN model effectively utilizes prior knowledge for improved CDR extraction.
  • KCN demonstrates the potential of integrating structured knowledge with unstructured text for biomedical NLP tasks.