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

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

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

Sort by

Same author

Inter-tissue relationships of gene expression in liver, muscle and adipose tissue of children with end-stage chronic liver disease.

Journal of pediatric gastroenterology and nutrition·2026

Same author

Multiomics analysis of primary metabolism reveals the genetic basis of nitrogen partitioning modulated by ZmAVT1A-1 in maize.

Nature genetics·2026

Same author

The heterogeneity of dopamine-mediated vasodilation in human intrarenal arteries.

Journal of advanced research·2026

Same author

An intelligent wearable device based on electrical impedance tomography for dynamic and accurate monitoring of voiding function in patients with benign prostatic hyperplasia: protocol for a single-centre self-controlled diagnostic consistency study.

Translational andrology and urology·2026

Same author

Denoising-Assisted Rapid Multiphoton Imaging for Analysing Traumatic Penumbra Microenvironment in Rat Brain.

Journal of biophotonics·2026

Same author

Phospho-JNK agonists show promising effects for the treatment of hepatocellular carcinoma.

iScience·2026

Search research articles

Related Experiment Video

Updated: Oct 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Hierarchical shared transfer learning for biomedical named entity recognition.

Zhaoying Chai¹, Han Jin¹, Shenghui Shi²

¹College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China.

BMC Bioinformatics

|January 5, 2022

Summary

This summary is machine-generated.

This study introduces hierarchical shared transfer learning to improve biomedical named entity recognition (BioNER). The novel approach enhances model generalization and stability for extracting medical entities from text.

Keywords:

BioNLP Biomedical named entity recognition Conditional random field Permutation language model Transfer learning

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Demonstrating Systematic Endobronchial Ultrasound to New Endoscopists

Author Spotlight: Demonstrating Systematic Endobronchial Ultrasound to New Endoscopists

Published on: August 11, 2023

Related Experiment Videos

Last Updated: Oct 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Demonstrating Systematic Endobronchial Ultrasound to New Endoscopists

Author Spotlight: Demonstrating Systematic Endobronchial Ultrasound to New Endoscopists

Published on: August 11, 2023

Area of Science:

Natural Language Processing
Bioinformatics
Machine Learning

Background:

Biomedical Named Entity Recognition (BioNER) is crucial for medical information extraction.
Deep learning models are prevalent in BioNER but suffer from poor generalization and instability.
Existing methods require improvement for robust medical entity identification.

Purpose of the Study:

To develop a novel approach for enhancing BioNER performance.
To improve the generalization and stability of deep learning models in BioNER.
To achieve state-of-the-art results in medical entity recognition.

Main Methods:

Proposing hierarchical shared transfer learning, combining multi-task learning and fine-tuning.
Implementing multi-level information fusion between entity and data features.
Training and evaluating the model on 14 diverse datasets covering 4 entity types.

Main Results:

Achieved state-of-the-art results on BC5CDR-chemical, BC5CDR-disease, and BC4CHEMD datasets.
Demonstrated significant F1-score improvements across multiple gold standard datasets compared to XLNet-CRF.
Investigated dataset-specific factors influencing performance, particularly for LINNAEUS.

Conclusions:

The proposed hierarchical shared transfer learning model exhibits superior medical entity recognition accuracy.
The approach significantly enhances model generalization and stability over traditional multi-task learning and fine-tuning.
The method offers a promising direction for advancing BioNER tasks in the medical domain.