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

Introduction to Language of Pathophysiology ll01:17

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...
Introduction to Language of Pathophysiology l01:25

Introduction to Language of Pathophysiology l

Pathophysiology investigates how biological mechanisms—typically starting at the cellular level—disrupt normal bodily functions. It bridges anatomy and physiology to explain the progression of disease. With this foundation, it is important to understand the following key terms used to describe disease processes: Diagnosis:The process of identifying a disease using clinical evaluation, including signs (objective evidence like rashes), symptoms (subjective experiences like pain), laboratory test...
Microbial Interactions: Parasitism01:22

Microbial Interactions: Parasitism

Parasitism is a form of microbial interaction in which parasitic microbes exploit a host organism for nutrients and shelter, often at the host's expense. Unlike mutualistic relationships, where both organisms benefit, parasitism benefits only the parasite and harms the host.Classification of ParasitesMicrobial parasites are broadly classified based on their location relative to the host.Ectoparasites remain on the host’s surface, such as the skin or outer tissues, drawing nutrients...

You might also read

Related Articles

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

Sort by
Same author

Assessing the Feasibility of Preoperative Axillary Ultrasound in Identifying Node-Negative Axillae: An Indian Retrospective Experience.

Diagnostics (Basel, Switzerland)·2026
Same author

Bioink-based 3D bioprinting: Paving the path for regenerative medicine.

International review of cell and molecular biology·2026
Same author

OncoPT: long-context transformer models for in hospital tumor phenotype extraction from pathology reports.

NPJ digital medicine·2026
Same author

Beyond gene length: Exon-intron architecture and isoform potential in the evolution of eukaryotic complexity.

bioRxiv : the preprint server for biology·2026
Same author

Place preference in a female adolescent rat model of neuropathic pain: The effects of cannabidiol and oxycodone on cerebellum cell density.

Psychopharmacology·2026
Same author

Evaluating the Use and Feasibility of Indocyanine Green (ICG) as a Beacon of Precision in Sentinel Node Biopsy for Breast Cancer from an Oncoplastic Practice in India.

Cancers·2026

Related Experiment Video

Updated: May 25, 2026

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
13:56

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

Literature mining of host-pathogen interactions: comparing feature-based supervised learning and language-based

Thanh Thieu1, Sneha Joshi, Samantha Warren

  • 1Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.

Bioinformatics (Oxford, England)
|January 31, 2012
PubMed
Summary

Automated methods were developed to detect and extract host-pathogen interactions (HPIs) from scientific literature. These approaches improve the collection of crucial data for understanding infectious diseases.

More Related Videos

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Generation of a Bovine Primary Enteroid-Derived Two-Dimensional Monolayer Culture System for Applications in Translational Biomedical Research
07:56

Generation of a Bovine Primary Enteroid-Derived Two-Dimensional Monolayer Culture System for Applications in Translational Biomedical Research

Published on: April 5, 2024

Related Experiment Videos

Last Updated: May 25, 2026

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
13:56

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Generation of a Bovine Primary Enteroid-Derived Two-Dimensional Monolayer Culture System for Applications in Translational Biomedical Research
07:56

Generation of a Bovine Primary Enteroid-Derived Two-Dimensional Monolayer Culture System for Applications in Translational Biomedical Research

Published on: April 5, 2024

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Infectious Disease Research

Background:

  • Host-pathogen interactions (HPIs) are vital in infectious diseases, involving complex protein exchanges.
  • Existing HPI data is fragmented across specialized databases, hindering comprehensive analysis.
  • Automated extraction of HPIs from biomedical literature is essential for a unified data repository.

Purpose of the Study:

  • To develop and compare automated methods for detecting and extracting HPI data from PubMed publications.
  • To identify host organisms, pathogen organisms, and involved proteins from text.
  • To enhance the creation of a centralized HPI knowledge base.

Main Methods:

  • A feature-based supervised learning approach using Support Vector Machines (SVMs).
  • Features included organism/protein names, HPI keywords, protein-protein interaction data, and experimental methods.
  • A language-based method utilizing a link grammar parser and semantic patterns.
  • Both methods were trained and tested on manually curated HPI data.

Main Results:

  • The developed approaches demonstrated higher accuracy and recall compared to existing methods.
  • The feature-based SVM approach achieved 66-73% accuracy in classifying HPI-containing publications.
  • Successful extraction of host/pathogen organisms and interacting proteins was demonstrated.

Conclusions:

  • Automated HPI extraction methods can significantly improve the consolidation of research data.
  • These tools facilitate a more efficient understanding of host-pathogen relationships in infectious diseases.
  • The developed methods offer a valuable resource for infectious disease research and drug development.