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Related Experiment Video

Updated: Jun 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Semi-supervised graph partitioning with decision trees.

Timothy Hancock¹, Hiroshi Mamitsuka

¹Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan. timhancock@kuicr.kyoto-u.ac.jp

Genome Informatics. International Conference on Genome Informatics

|May 9, 2009

Summary

This summary is machine-generated.

This study introduces decision trees for graph partitioning, optimizing sub-graph searches within adjacency matrices. This novel approach enhances classification accuracy and identifies key variables for tumor diagnosis using gene expression data.

Related Experiment Videos

Last Updated: Jun 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Computational biology
Machine learning
Graph theory

Background:

Graph partitioning is crucial for analyzing complex networks, but traditional methods face challenges in optimizing sub-graph identification and evaluating solution quality.
Decision trees offer a predictive framework for evaluating graph partitioning solutions, determining the optimal number of sub-graphs, and assessing variable importance.

Purpose of the Study:

To introduce and evaluate a novel framework for graph partitioning utilizing decision trees.
To assess the performance of decision tree-based graph partitioning for multiclass classification using gene expression data.
To compare three graph cut indices: ratio cut, normalized cut, and network modularity.

Main Methods:

A new framework for graph partitioning was developed using decision trees to recursively search for sub-graphs within a graph adjacency matrix.
The framework was evaluated on a benchmark dataset for multiclass classification of tumor diagnosis based on gene expression.
Performance was assessed using classification accuracy, the ability to estimate the optimal number of sub-graphs, and the capacity to identify important variables.

Main Results:

Decision tree-based graph partitioning demonstrated effectiveness in multiclass classification tasks.
The framework showed promise in estimating the optimal number of sub-graphs and identifying key variables relevant to tumor diagnosis.
Comparative analysis of ratio cut, normalized cut, and network modularity provided insights into their performance within this new framework.

Conclusions:

Decision trees offer a powerful and predictive approach to graph partitioning, enhancing analytical capabilities in complex datasets.
This method holds significant potential for applications in bioinformatics, particularly for disease diagnosis and biomarker discovery.
The framework's ability to assess variable importance aids in understanding the underlying biological mechanisms driving tumor classification.