Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Rapid Identification of Pathogens

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

You might also read

Related Articles

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

Sort by

Same author

Efficacy and Long-Term Outcomes of Direct-Acting Antivirals in Patients with Hepatitis C Virus-Related Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis.

Gut and liver·2026

Same author

Dynamic Contrast-Enhanced pituitary MRI with higher temporal resolution using deep learning Reconstruction: A feasibility study.

European journal of radiology·2026

Same author

Sex-specific Trends in Methamphetamine Use in the United States, 2002-2022.

Journal of addiction medicine·2026

Same author

Clinical Terminology Mapping Service Based on Information Retrieval.

Methods of information in medicine·2026

Same author

Hematology-centered physiological remodeling of black seabream (Acanthopagrus schlegelii) under gradual water temperature fluctuations.

Fish & shellfish immunology·2026

Same author

Effects of natural mono- and di-saccharide as alternative sweeteners on inflammatory bowel disease: a narrative review.

Korean journal of community nutrition·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

POSBIOTM-NER: a trainable biomedical named-entity recognition system.

Yu Song¹, Eunju Kim, Gary Geunbae Lee

¹Department of CSE, POSTECH Pohang, 790-784, Korea. songyu@postech.ac.kr

Bioinformatics (Oxford, England)

|April 9, 2005

Summary

This summary is machine-generated.

POSBIOTM-NER is a trainable biomedical named-entity recognition system that adapts to new datasets using machine learning. This system demonstrates robust performance across multiple corpora for enhanced biological data analysis.

More Related Videos

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Related Experiment Videos

Last Updated: Jun 17, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Area of Science:

Biomedical informatics
Natural Language Processing
Machine Learning

Background:

Named-entity recognition (NER) is crucial for extracting biological information from text.
Developing adaptable NER systems is essential for handling diverse biomedical datasets.
Existing systems may require extensive retraining for new data types.

Purpose of the Study:

To introduce POSBIOTM-NER, a novel trainable biomedical named-entity recognition system.
To demonstrate the system's ability to adapt to new datasets without performance loss.
To facilitate automated information extraction for specific biological pathways.

Main Methods:

Utilized Conditional Random Field (CRF) machine learning techniques.
Employed automatic linguistic feature analysis for system adaptation.
Trained the system on three distinct datasets: GENIA Corpus, BioCreative data, and a custom POSBIOTM/NE corpus.

Main Results:

POSBIOTM-NER proved to be a highly trainable and adaptable system.
The system maintained performance integrity when trained on new datasets.
Successful training was achieved on GENIA-NER, GENE-NER, and GPCR-NER datasets.

Conclusions:

POSBIOTM-NER offers a flexible and efficient solution for biomedical named-entity recognition.
The system's adaptability reduces the need for manual feature engineering.
POSBIOTM-NER is poised for application in areas like GPCR-related pathway extraction.