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

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports07:35

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

2.1K
A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and...
2.1K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

939
This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and...
939
A Web Tool for Generating High Quality Machine-readable Biological Pathways08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

18.5K
PathWhiz is a comprehensive, online pathway drawing tool for generating biochemical and biological pathways. It uses publicly accessible databases and easily expandable palettes consisting of pre-drawn pathway components. This protocol describes how to easily build new pathways, replicate and edit existing pathways, and propagate previously drawn pathways to different...
18.5K
Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

1.6K
The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance...
1.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

7.4K
This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...
7.4K
Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning11:02

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

24.4K
Vagus nerve stimulation (VNS) has emerged as a tool to induce targeted synaptic plasticity in the forebrain to modify a range of behaviors. This protocol describes how to implement VNS to facilitate the consolidation of fear extinction...
24.4K

You might also read

Related Articles

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

Sort by
Same author

Filling the mechanistic and methodological gaps for predicting cytokine release syndrome in humans.

Frontiers in pharmacology·2026
Same author

Influence of oven-drying pre-treatment on the metabolic profile of extracts from brewer's spent grains.

Frontiers in nutrition·2026
Same author

momapy: a Python library to work with molecular maps.

Bioinformatics (Oxford, England)·2026
Same author

Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework.

Journal of chemical theory and computation·2026
Same author

Decoding Allosteric Grammar with Explainable AI Integrating Protein Language Models and Energy Landscape Analysis: Neutral Frustration at Allosteric Binding Sites Encodes Regulatory Versatility in Protein Kinases.

bioRxiv : the preprint server for biology·2026
Same author

Phylum-wide propionate degradation and its potential connection to poly-gamma-glutamate biosynthesis in Candidatus Cloacimonadota phylum.

The ISME journal·2026
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Jan 20, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

2.1K

Machine Learning to Support the Presentation of Complex Pathway Graphs.

Sune S Nielsen, Marek Ostaszewski, Fintan McGee

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 5, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Machine learning, specifically Support Vector Machines (SVMs), can automate node duplication in biological pathway diagrams. This approach effectively learns human preferences, improving the efficiency of pathway visualization and curation.

    More Related Videos

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    939
    A Web Tool for Generating High Quality Machine-readable Biological Pathways
    08:01

    A Web Tool for Generating High Quality Machine-readable Biological Pathways

    Published on: February 8, 2017

    18.5K

    Related Experiment Videos

    Last Updated: Jan 20, 2026

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

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

    Published on: October 13, 2023

    2.1K
    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    939
    A Web Tool for Generating High Quality Machine-readable Biological Pathways
    08:01

    A Web Tool for Generating High Quality Machine-readable Biological Pathways

    Published on: February 8, 2017

    18.5K

    Area of Science:

    • Systems Biology
    • Computational Biology
    • Bioinformatics

    Background:

    • Biological pathway visualization is crucial for understanding complex biological systems.
    • Manual pathway layout and curation are time-consuming and challenging.
    • Node duplication improves diagram readability by reducing edge crossings and lengths.

    Purpose of the Study:

    • To develop a Machine Learning (ML) approach to automate node duplication in biological pathway diagrams.
    • To train models based on expert manual curation actions.
    • To assess the effectiveness of ML in emulating human node duplication preferences.

    Main Methods:

    • Training a Support Vector Machine (SVM) using incremental snapshots of manually curated pathway diagrams.
    • Applying trained SVM models to large and medium-sized biological pathway instances.
    • Conducting a user validation study comparing model predictions with human expert choices.

    Main Results:

    • The trained SVM models successfully predicted nodes for duplication in biological pathways.
    • The ML approach emulated human choices in node duplication.
    • Model predictions aligned with user validation study outcomes across varying user experience levels.

    Conclusions:

    • The proposed ML approach effectively learns and replicates human-like node duplication preferences.
    • This method can significantly support and streamline the curation of biological pathway diagrams.
    • The approach demonstrates potential for application in various contexts of pathway visualization and analysis.