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

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Published on: May 17, 2019

Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers.

Hualong Yu¹, Shufang Hong, Xibei Yang

¹School of Computer Science and Engineering, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China.

Biomed Research International

|October 1, 2013

Summary

This summary is machine-generated.

This study introduces an ensemble learning method to address skewed multiclass cancer DNA microarray datasets. The novel approach effectively handles class imbalance for accurate molecular cancer diagnosis.

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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:

Bioinformatics
Computational Biology
Machine Learning

Background:

DNA microarrays enable simultaneous measurement of gene activity for molecular cancer diagnosis.
Skewed datasets in DNA microarray data pose challenges for traditional learning algorithms, leading to inaccurate results.
Existing research often overlooks multiclass imbalance problems, focusing primarily on binary classifications.

Purpose of the Study:

To address the multiclass imbalanced classification problem in cancer DNA microarray data.
To develop a robust ensemble learning strategy for accurate cancer diagnosis from skewed datasets.
To improve the performance of classification algorithms on imbalanced biological data.

Main Methods:

Employed an ensemble learning approach with an one-against-all coding strategy to convert multiclass problems into binary ones.
Utilized an evolving version of random subspace to generate diverse training subsets with feature subspaces.
Integrated decision threshold adjustment or random undersampling techniques within each subset to mitigate class imbalance.
Implemented Support Vector Machine as the base classifier and introduced a novel 'counter voting' rule for final decision-making.

Main Results:

The proposed ensemble learning methods demonstrated insensitivity to class imbalance in skewed multiclass cancer microarray datasets.
Experimental results on eight datasets confirmed the effectiveness of the approach compared to traditional classification methods.
The novel 'counter voting' rule contributed to robust final decisions in the ensemble framework.

Conclusions:

The developed ensemble learning strategy effectively handles multiclass imbalanced classification problems in cancer DNA microarrays.
The methods provide a reliable solution for accurate molecular cancer diagnosis, even with highly skewed data.
This research offers a significant advancement in applying machine learning to complex biological datasets.