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

Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...

You might also read

Related Articles

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

Sort by
Same author

UM-CPP: A Universal Model for Efficient Classification of Protein Particles in cryo-EM Micrographs with Feature Engineering.

ACS omega·2025
Same author

Local and distant atrophy mediate the relationship between tau pathology and cognition in temporoparietal region in Alzheimer's disease.

Journal of Alzheimer's disease : JAD·2025
Same author

Edaravone Protects Trophoblast Cells From Hypoxic Injury in Preeclampsia: Inhibition of the PI3K/AKT Pathway as a Promising Therapeutic Approach.

Immunity, inflammation and disease·2024
Same author

3D-FVS: construction and application of three-dimensional fundus vascular structure model based on single image features.

Eye (London, England)·2022
Same author

Few-mode fiber based Raman distributed temperature sensing.

Optics express·2017
Same author

Tripodal S-Ligand Complexes of Copper(I) as Catalysts for Alkene Aziridination, Sulfide Sulfimidation, and C-H Amination.

Inorganic chemistry·2017
Same journal

Prostate Cancer Detection in Urine Using the Fusion of LIBS, FTIR Dual Spectra and FTIR Reconstructed Image.

Analytical chemistry·2026
Same journal

An In Situ Gelled PCA-Na/Gelatin Hydrogel Plant Wearable Sensor Patch Based on Synergistic Enhancement Strategy for Atrazine Detection.

Analytical chemistry·2026
Same journal

Enzymatic Deacetylation as a Selective Strategy for <i>O</i>-GlcNAc Identification.

Analytical chemistry·2026
Same journal

The Cerebrospinal Fluid Proteome of Alzheimer's Disease Patients: Differentiation and Prediction of Responders and Non-Responders to Lymphatic-Venous Anastomosis.

Analytical chemistry·2026
Same journal

Profiling Protein Aggregate Size Using Single-Molecule Array Technology.

Analytical chemistry·2026
Same journal

Single-Particle Isotopic Analysis Using the Liquid Sampling-Atmospheric Pressure Glow Discharge Microplasma Coupled to an Orbitrap Mass Spectrometer.

Analytical chemistry·2026
See all related articles
  1. Home
  3. Sibiox: A Matrix Based Bioinformatics Analysis Tool Based On Swarm Intelligence Algorithm.
  1. Home
  3. Sibiox: A Matrix Based Bioinformatics Analysis Tool Based On Swarm Intelligence Algorithm.

Related Experiment Video

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

7.6K

SIBioX: A Matrix Based Bioinformatics Analysis Tool Based on Swarm Intelligence Algorithm.

Zhaomin Yao1,2, Haonan Shangguan3, Weiming Xie1

  • 1Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China.

Analytical Chemistry
|March 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

SIBioX is a new bioinformatics tool that uses swarm intelligence to analyze biological matrix data. It effectively reduces data dimensionality and improves feature selection accuracy for efficient biomedical research.

More Related Videos

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro
11:21

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Published on: February 16, 2020

5.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.4K

Related Experiment Videos

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

7.6K
Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro
11:21

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Published on: February 16, 2020

5.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.4K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Swarm Intelligence

Background:

  • Biological matrix data are crucial for computational analysis but suffer from high dimensionality.
  • The curse of dimensionality increases computational complexity and risk of overfitting.
  • Existing methods struggle with efficient analysis of complex biological datasets.

Purpose of the Study:

  • To develop a novel matrix-based bioinformatics tool, SIBioX, for efficient analysis of biological data.
  • To integrate swarm intelligence algorithms for enhanced feature selection and dimensionality reduction.
  • To provide a user-friendly platform for comprehensive biological data analysis.

Main Methods:

  • Developed SIBioX, a tool integrating 54 swarm intelligence methods, 5 feature selection techniques, and 17 machine learning models.
  • Implemented feature normalization, selection, classification, clustering, statistical analysis, and data visualization.
  • Enabled conversion of non-matrix biological data (sequences) into matrix formats.

    • Main Results:

    • SIBioX demonstrated high accuracy in feature selection.
    • The tool effectively reduced data dimensionality, mitigating the curse of dimensionality.
    • Streamlined bioinformatics workflows and enhanced efficiency in biomedical research.

    Conclusions:

    • SIBioX offers a powerful and efficient solution for analyzing high-dimensional biological matrix data.
    • The integration of swarm intelligence significantly improves analytical performance.
    • SIBioX promotes greater efficiency and accuracy in biomedical research applications.