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

You might also read

Related Articles

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

Sort by

Same author

Quantifying the Independent and Combined Contributions of Clinical, Genetic, Proteomic, and Metabolomic Signals to ASCVD Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same author

Cellular immunology data enable clinical severity prediction via supervised machine learning.

iScience·2026

Same author

Dual-Layer Grain-Boundary In Situ Polymerization Modulates Elastic Modulus for Mechanically Stable Flexible All-Perovskite Tandem Solar Cells.

ACS applied materials & interfaces·2026

Same author

High-throughput microbiome profiling and co-occurrence with antibiotic-resistance genes in Lucilia sericata.

FEMS microbiology letters·2026

Same author

Synergistic bone regeneration through sequential dual-drug delivery.

Biomedical engineering letters·2026

Same author

Comparative Analysis of Gut Eukaryotic Communities in Three Laboratory-Reared Cockroach Species Using Metabarcoding.

The Journal of eukaryotic microbiology·2026

Same journal

Established machine learning matches tabular foundation models in clinical predictions.

BMC medical informatics and decision making·2026

Same journal

Explainable AI machine learning framework for chronic kidney disease prediction utilizing electronic health records.

BMC medical informatics and decision making·2026

Same journal

Interpretable SHAP-based machine learning framework for patient satisfaction prediction: a case study in Thammasat University Hospital.

BMC medical informatics and decision making·2026

Same journal

Automated generation of structured breast ultrasound reports using BreastViT and ChatGPT.

BMC medical informatics and decision making·2026

Same journal

Shared decision-making and medication adherence among community adults with chronic diseases: a cross-sectional study in Hubei Province, China.

BMC medical informatics and decision making·2026

Same journal

Classification of periapical radiographic findings for root canal therapy decision support using deep neural networks.

BMC medical informatics and decision making·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 1, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

An inference method from multi-layered structure of biomedical data.

Myungjun Kim¹, Yonghyun Nam¹, Hyunjung Shin²

¹Department of Industrial Engineering, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon, 16499, South Korea.

BMC Medical Informatics and Decision Making

|May 26, 2017

Summary

This summary is machine-generated.

This study introduces a novel semi-supervised learning algorithm for inferring biological system components across multiple omics layers. The method significantly improves disease co-occurrence prediction accuracy compared to single-layer approaches.

Keywords:

Disease co-occurrence prediction Integrative inference on biomedical data Semi-supervised learning Semi-supervised learning for multiple networks Symptom-disease multi-layered network

More Related Videos

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Mar 1, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Biological systems are complex, multi-layered structures involving various omics (genome, epigenome, transcriptome, etc.) and clinical data (diseases, symptoms).
Inferring unknown biological components or traits from known data is a key challenge.
Existing methods often integrate omics data in parallel, neglecting vertical relationships.

Purpose of the Study:

To develop a novel semi-supervised learning algorithm for inferring components within multi-layered biological systems.
To validate the algorithm's effectiveness in predicting disease co-occurrence.

Main Methods:

Development of a semi-supervised learning algorithm tailored for multi-layered complex systems.
Application of the algorithm to a two-layered network (symptom-disease) for disease co-occurrence prediction.

Main Results:

The symptom-disease layered network achieved an Area Under the Curve (AUC) of 0.74, a significant improvement over the single-layered disease network's AUC of 0.59.
The proposed method shows potential for further improvements when applied to the entire omics data structure.

Conclusions:

The research presents a novel integrative algorithm that leverages the vertical structure of omics data.
The findings offer enhanced guidelines for disease co-occurrence prediction.
The algorithm serves as a valuable tool for inferring relationships within multi-layered biological systems.