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Protein Networks

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,...

Protein Networks

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.
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Ligand Binding and Linkage

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...

Gene Families

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...

Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Related Experiment Video

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection.

Andrew Arnold, William W Cohen

WASA ... : International Conference on Wireless Algorithms, Systems, and Applications : Proceedings. WASA

|January 15, 2011

Summary

This summary is machine-generated.

This study shows that analyzing author connections and citation networks can effectively predict future gene research in academic publications. Social network analysis outperforms methods using only biological data.

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Area of Science:

Biomedical Informatics
Bibliometrics
Computational Biology

Background:

Identifying gene mentions in biomedical literature is crucial for research.
Publication metadata, including citation networks and author collaborations, contains rich information.
Predicting future research trends can accelerate scientific discovery.

Purpose of the Study:

To evaluate the utility of publication-related metadata for gene identification in academic papers.
To determine if social network analysis of authors and their publications can predict future gene research.
To compare social-network based link prediction with non-social biological information methods.

Main Methods:

Framing gene identification as a link prediction problem between authors and genes.
Utilizing citation networks, coauthorship information, and curated databases.
Developing and testing a social-network based link prediction model.
Comparing model performance against various baseline methods.

Main Results:

Social-network based link prediction effectively identifies genes in academic publications.
The proposed method significantly outperforms baseline approaches.
Information on author's past research, coauthors, and citations improves prediction accuracy.
This approach is more effective than methods relying solely on non-social biological data.

Conclusions:

Publication metadata, particularly social network information, is highly valuable for gene identification.
Social network analysis offers a powerful tool for predicting future gene research trends.
This methodology advances the field of biomedical informatics and bibliometrics.