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Related Experiment Video

Updated: Mar 8, 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

Meta-analytic support vector machine for integrating multiple omics data.

SungHwan Kim^1,2, Jae-Hwan Jhong¹, JungJun Lee¹

¹Department of Statistics, Korea University, Anam-dong, Seoul, 136-701 South Korea.

|February 3, 2017

Summary

This summary is machine-generated.

We developed a novel Meta-SVM method to analyze multiple omics datasets, improving gene discovery for diseases. This approach enhances reproducibility and statistical power for identifying consensus disease-associated genes.

Keywords:

Data integration Meta-analysis Support vector machine TCGA

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

Bioinformatics
Genomics
Systems Biology

Background:

High-throughput omics data are crucial for disease biomarker and process monitoring.
Support Vector Machines (SVM) are widely used for gene selection.
Single-data analysis faces limitations in reproducibility and statistical power.

Purpose of the Study:

To propose a meta-analytic SVM (Meta-SVM) for integrating multiple omics data.
To enhance the detection of consensus genes associated with diseases across studies.

Main Methods:

Developed a novel Meta-SVM algorithm.
Applied Meta-SVM to diverse omics data from breast cancer (TCGA) and idiopathic pulmonary fibrosis (IPF).

Main Results:

Meta-SVM demonstrated superiority over existing methods in identifying true signal genes.
Identified gene sets consistently associated with breast cancer and IPF across studies.
Discovered significant enrichment of ABC transporters pathways in breast cancer data, relevant to disease mechanisms.

Conclusions:

Meta-SVM effectively leverages multiple omics data for robust meta-analysis.
Facilitates the identification of potential biomarkers and elucidation of disease processes.