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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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.
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Protein Networks02:26

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Nucleic Acid Structure01:25

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA has a double-helix structure. The...

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Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.

A M Cohen1, W R Hersh, C Dubay

  • 1Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S,W, Sam Jackson Park Road, Portland, Oregon 97239-3098, USA. cohenaa@ohsu.edu

BMC Bioinformatics
|April 26, 2005
PubMed
Summary

This study introduces a novel text-mining approach for extracting gene and protein name synonyms. The method uses network analysis of symbol co-occurrences, achieving comparable performance to existing techniques with minimal seed data.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Biomedical researchers face information overload from extensive text data.
  • Text-mining offers a solution for extracting valuable knowledge.
  • Novel methods are needed to enhance knowledge extraction capabilities.

Purpose of the Study:

  • To develop and evaluate a new text-mining method for gene and protein name synonym extraction.
  • To leverage network structure analysis of symbol co-occurrences for improved knowledge extraction.
  • To assess the method's efficiency and performance in identifying synonyms.

Main Methods:

  • A novel text-mining approach analyzing network structures of symbol co-occurrences.
  • Application to the automatic extraction of gene and protein name synonyms.
  • Evaluation on approximately 50,000 MEDLINE abstracts using curated genomics databases as a gold standard.

Main Results:

  • The system achieved a maximum F-score of 22.21% (23.18% precision, 21.36% recall).
  • Demonstrated high efficiency in the utilization of seed pairs.
  • Performance was evaluated on a large dataset of biomedical literature.

Conclusions:

  • The developed text-mining method performs comparably to existing approaches.
  • The method does not require sophisticated named-entity recognition (NER) tools.
  • It necessitates minimal initial seed knowledge for effective operation.