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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

Case-based retrieval framework for gene expression data.

Ali Anaissi¹, Madhu Goyal¹, Daniel R Catchpoole²

¹Center for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, New South Wales, Australia.

Cancer Informatics

|April 11, 2015

Summary

This summary is machine-generated.

This study introduces a case-based retrieval framework using k-nearest neighbors and weighted features to find similar patients based on gene expression profiles. The method achieved high accuracy across multiple cancer datasets, aiding diagnosis.

Keywords:

case base reasoning data mining dimensionality reduction feature weighting gene expression machine learning

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Last Updated: Apr 15, 2026

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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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Published on: August 16, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene expression data analysis presents challenges in case-based reasoning due to high dimensionality.
Similarity measures for complex, high-dimensional gene expression datasets require advanced machine learning techniques.
Feature selection and dimensionality reduction are crucial for effective gene expression similarity measurements.

Purpose of the Study:

To propose a novel case-based retrieval framework for gene expression data.
To enhance the accuracy of retrieving similar patient cases based on gene expression profiles.

Main Methods:

Utilized a k-nearest-neighbor classifier.
Employed a weighted-feature-based similarity measure for case retrieval.
Developed a case-based retrieval framework tailored for gene expression data.

Main Results:

Achieved 96% accuracy on a childhood leukemia dataset.
Demonstrated high performance on other datasets: 95% (NCI), 93% (Colon cancer), and 98% (Prostate cancer).
Successfully retrieved similar patients based on gene expression profiles across multiple cancer types.

Conclusions:

The proposed framework is effective for retrieving similar patients using gene expression data.
This approach supports improved diagnosis and treatment, particularly for childhood leukemia.
The framework is adaptable for application to diverse gene expression datasets.