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Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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Related Experiment Video

Updated: Aug 27, 2025

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

Multi-omics data integration for hepatocellular carcinoma subtyping with multi-kernel learning.

Jiaying Wang¹, Yuting Miao², Lingmei Li²

¹Department of Respiratory, Gastroenterology and Oncology (West Branch), The Second Hospital of Shanxi Medical University, Taiyuan, China.

Frontiers in Genetics

|September 23, 2022

Summary

This summary is machine-generated.

This study identifies two distinct hepatocellular carcinoma (HCC) subtypes using multi-omics data. One subtype shows significantly higher mortality, paving the way for precision medicine in liver cancer treatment.

Keywords:

biomarkers multiple kernel learning omics data integration rMKL-LPP subtype identification

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Hepatocellular carcinoma (HCC) presents high mortality and varied clinical features.
Identifying molecular subtypes is crucial for effective HCC treatment and improved patient survival.

Purpose of the Study:

To integrate multi-omics data (mRNA, miRNA, DNA methylation) for HCC subtype identification.
To correlate identified subtypes with patient survival and clinical outcomes.

Main Methods:

Applied regularized multiple kernel learning with locality preserving projections.
Integrated mRNA, miRNA, and DNA methylation data.
Utilized weighted gene co-expression network analysis and hub gene analysis.

Main Results:

Identified two HCC subtypes significantly associated with overall survival.
The high-risk subtype exhibited a 3-year mortality rate of 51.0% vs. 23.5% in the low-risk subtype.
Discovered significant differences in gene expression, methylation, pathway activity, and immune cell infiltration between subtypes.
Identified key prognostic genes including CDK1, CDCA8, TACC3, and NCAPG.

Conclusions:

The identified HCC subtypes offer novel insights into disease heterogeneity.
Findings support the potential for precision medicine strategies in HCC management.
The study highlights specific molecular pathways and immune cell profiles associated with HCC prognosis.