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Updated: Mar 3, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker

Nathaniel M Crabtree¹, Jason H Moore², John F Bowyer³

¹Bioinformatics, Department of Information Science, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Joint Bioinformatics Graduate Program, Little Rock, AR USA.

|April 29, 2017

Summary

This summary is machine-generated.

A new multi-class computational evolution system (CES) effectively identifies biomarkers in complex datasets. This advanced CES balances accuracy and model complexity, outperforming other methods on smaller datasets.

Keywords:

Artificial intelligence Biomarker discovery Classification Data mining Evolutionary algorithm Feature selection Genetic programming Machine learning Multi-class

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Last Updated: Mar 3, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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Published on: May 28, 2021

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Area of Science:

Computational biology
Bioinformatics
Machine learning

Background:

Computational evolution systems (CES) are knowledge discovery engines for identifying complex relationships in large datasets.
Pareto optimization in CES balances classifier accuracy with model complexity, enabling the selection of minimal, non-redundant features.
Existing CES were limited to binary classification; this study introduces a multi-class CES.

Purpose of the Study:

To develop and evaluate a multi-class extension of the computational evolution system (CES).
To assess the performance of the multi-class CES in identifying biomarkers from complex, multi-class datasets.

Main Methods:

The multi-class CES was developed and compared against Support Vector Machine (SVM), Random k-Nearest Neighbor (RKNN), and Random Forest (RF) algorithms.
Performance was evaluated on three distinct multi-class RNA sequencing datasets.
Key metrics included run-time, classification accuracy, number of selected features, and feature set stability (Tanimoto distance).

Main Results:

Algorithm performance varied across datasets.
The multi-class CES demonstrated superior performance on the dataset with the smallest sample size.
This highlights a unique advantage of CES when dealing with limited sample sizes, where other methods often experience accuracy degradation.

Conclusions:

The multi-class CES enhances the utility of CES for analyzing complex, multi-class biological data.
This advancement facilitates the identification of critical biomarkers and features in diverse datasets.