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Related Experiment Video

Updated: Aug 29, 2025

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

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Published on: December 28, 2015

Predict DLBCL patients' recurrence within two years with Gaussian mixture model cluster oversampling and multi-kernel

Meng Xing¹, Yanbo Zhang¹, Hongmei Yu¹

¹Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China.

Computer Methods and Programs in Biomedicine

|September 11, 2022

Summary

A new model predicts diffuse large B-cell lymphoma (DLBCL) relapse within two years. The GMM-SENN-MKL method improves prediction accuracy by handling data imbalance and heterogeneity for better patient outcomes.

Keywords:

Class imbalance DLBCL Gaussian mixture model clustering oversampling Multiple kernel learning Recurrence prediction

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Hematology
Oncology
Machine Learning in Medicine

Background:

Diffuse large B-cell lymphoma (DLBCL) is a common non-Hodgkin's lymphoma in adults.
Early relapse (within two years) is associated with a poor prognosis.
Accurate prediction of early relapse is crucial for timely intervention and improved patient management.

Purpose of the Study:

To develop a predictive model for identifying diffuse large B-cell lymphoma (DLBCL) patients at high risk of relapse within two years.
To provide clinicians with a tool for individualized treatment strategies based on relapse risk.
To address challenges of data class imbalance and clinical heterogeneity in DLBCL datasets.

Main Methods:

A secondary-level class imbalance method using Gaussian mixture model (GMM) clustering and resampling (SMOTEENN) was employed.
A multi-kernel support vector machine (SVM) was utilized to integrate heterogeneous clinical data.
The combined approach (GMM-SENN-MKL) was developed to identify patients likely to relapse within two years.

Main Results:

The Inverse Weighted -GMM +SMOTEENN method demonstrated superior performance compared to standard methods.
This approach showed an 8.75% increase in Area Under the ROC Curve (AUC) and reduced ECE and Brier scores.
Multiple kernel learning (MKL) achieved the best discrimination and calibration, with maximized AUC, accuracy, recall, precision, and F1 scores.

Conclusions:

The developed inverse weighted -GMM+SMOTEENN+MKL (GMM-SENN-MKL) method effectively handles class imbalance and data heterogeneity in DLBCL.
This model shows significant potential for accurately predicting recurrence in DLBCL patients within two years.
The GMM-SENN-MKL method offers a valuable tool for clinical decision-making and personalized treatment planning.