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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

MSC-CSMC: A multi-objective semi-supervised clustering algorithm based on constraints selection and multi-source

Zeyuan Wang¹, Hong Gu¹, Minghui Zhao¹

¹Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China.

Frontiers in Genetics

|March 16, 2023

Summary

This summary is machine-generated.

This study introduces a novel semi-supervised clustering algorithm (MSC-CSMC) that integrates multi-source constraints and selects optimal pairwise constraints. This approach enhances gene expression data clustering by mitigating noisy constraints and improving overall performance.

Keywords:

constraint selection gene expression data multi-objective optimization multi-source constraints semi-supervised clustering

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Gene expression data analysis often employs clustering techniques to group genes.
Semi-supervised clustering methods leverage pairwise constraints to enhance performance.
Noisy constraints and lack of multi-source information integration hinder existing semi-supervised clustering algorithms.

Purpose of the Study:

To propose a novel multi-objective semi-supervised clustering algorithm (MSC-CSMC) for unlabeled gene expression data.
To address the limitations of noisy constraints and integrate multiple information sources for improved clustering quality.
To enhance the performance of gene clustering by effectively selecting and utilizing constraints.

Main Methods:

Developed a multi-objective semi-supervised clustering algorithm (MSC-CSMC) incorporating constraints selection and multi-source constraints.
Utilized gene expression data and Gene Ontology (GO) to form multi-source constraints.
Implemented a multi-objective evolutionary framework with mixed chromosome encoding for synergistic optimization and noisy constraint reduction.

Main Results:

The proposed MSC-CSMC algorithm demonstrated superior performance in gene expression data clustering.
Effective integration of multi-source constraints improved clustering quality.
The constraints selection mechanism successfully reduced the negative impact of noisy constraints.

Conclusions:

The MSC-CSMC algorithm offers a robust solution for gene expression data clustering.
Integrating multi-source constraints and employing a constraints selection strategy are effective for enhancing semi-supervised clustering.
The developed method shows significant potential for biological data analysis.