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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

An Efficient Binary Sand Cat Swarm Optimization for Feature Selection in High-Dimensional Biomedical Data.

Elnaz Pashaei^1,2

¹Department of Computer Engineering, Istanbul Aydin University, Istanbul 34295, Turkey.

Bioengineering (Basel, Switzerland)

|October 28, 2023

Summary

This summary is machine-generated.

A new algorithm, PILC-BSCSO, effectively addresses the curse of dimensionality in biomedical big data. This advanced feature selection method improves disease diagnosis and patient care by identifying key genes for conditions like colon cancer and liver cancer.

Keywords:

biomedical data cancer prediction feature selection pinhole-imaging-based learning sand cat swarm optimization

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Biomedical data analysis
Bioinformatics
Machine learning in healthcare

Background:

Big data in biomedicine offers potential for disease diagnosis and patient care.
The curse of dimensionality poses a significant challenge in analyzing complex biomedical datasets.
Effective feature selection is crucial for extracting relevant information from high-dimensional biomedical data.

Purpose of the Study:

To introduce a novel algorithm, PILC-BSCSO, for enhanced feature selection in biomedical big data.
To address the curse of dimensionality using a pinhole-imaging-based learning strategy and crossover operator.
To improve the accuracy of disease diagnosis and patient care management through effective data analysis.

Main Methods:

Developed PILC-BSCSO by integrating a pinhole-imaging learning strategy and crossover operator into the Binary Sand Cat Swarm Optimization (BSCSO) algorithm.
Employed a Support Vector Machine (SVM) classifier with a linear kernel to evaluate classification accuracy.
Validated the algorithm using three public medical datasets, including colon cancer and Liver Hepatocellular Carcinoma (TCGA-HCC) data.

Main Results:

PILC-BSCSO outperformed 11 state-of-the-art techniques in classification accuracy and feature selection.
Achieved 100% classification accuracy for colon cancer using only 10 genes.
Identified a five-gene subset, including HMMR, CHST4, and COL15A1, with strong predictive potential for liver cancer from TCGA data.

Conclusions:

PILC-BSCSO is a highly effective method for feature selection in high-dimensional biomedical data.
The algorithm demonstrates significant improvements in disease classification accuracy, particularly for challenging cases like colon cancer.
PILC-BSCSO holds promise for advancing personalized medicine and improving patient outcomes through precise biomarker identification.