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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Related Experiment Video

Updated: Jul 6, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

A combination of rough-based feature selection and RBF neural network for classification using gene expression data.

J H Chiang1, S H Ho

  • 1Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, Republic of China. jchiang@mail.ncku.edu.tw

IEEE Transactions on Nanobioscience
|March 13, 2008
PubMed
Summary

This study introduces a new rough-based feature selection method for gene expression data. This approach enhances prediction accuracy when combined with neural networks, outperforming other methods.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Related Experiment Videos

Last Updated: Jul 6, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Machine Learning
  • Computational Biology

Background:

  • Gene expression data analysis is crucial for understanding biological processes.
  • Effective feature selection is vital for accurate classification and prediction in genomics.
  • Existing methods may require prior knowledge of cluster numbers or struggle with identifying relevant features.

Purpose of the Study:

  • To propose a novel rough-based feature selection method for gene expression data.
  • To integrate this method with a radial basis function neural network for a prediction scheme.
  • To evaluate its performance against other feature selection techniques and classifiers.

Main Methods:

  • A rough-based feature selection algorithm was developed to identify relevant genes without a priori cluster information.
  • This method was combined with a radial basis function neural network (RBFNN).
  • Performance was compared using Naive Bayes and linear Support Vector Machine (SVM) classifiers against Information Gain and Principal Component Analysis (PCA).

Main Results:

  • The proposed rough-based feature selection method, when combined with RBFNN, achieved high classification accuracy rates.
  • It demonstrated superior performance compared to other feature selection methods and classifiers on published datasets.
  • The method effectively identifies relevant features and approximates cluster centers without prior knowledge.

Conclusions:

  • The novel rough-based feature selection method offers a robust approach for gene expression data analysis.
  • Its integration with RBFNN provides an effective prediction scheme with high accuracy.
  • This technique holds promise for advancing genomic data interpretation and biomarker discovery.