Discretization of gene expression data revised

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene expression data is vital for understanding gene interactions and functions.
• Data mining and machine learning algorithms often require data discretization for gene expression analysis.
• The choice of discretization method significantly impacts the performance of inference algorithms.

Purpose of the Study:

• To review current state-of-the-art discretization techniques for gene expression data.
• To identify key factors influencing the selection of discretization methods.
• To provide guidance for optimizing gene expression data analysis.

Main Methods:

• Comprehensive literature review of discretization techniques.
• Analysis of challenges and considerations in applying discretization to gene expression data.
• Synthesis of current knowledge on discretization approaches.

Main Results:

• Identified various discretization methods applicable to gene expression data.
• Highlighted the critical role of discretization in gene interaction inference.
• Outlined essential criteria for selecting appropriate discretization strategies.

Conclusions:

• Effective discretization is paramount for accurate gene expression data analysis.
• Careful selection of discretization techniques enhances the reliability of machine learning models.
• This review serves as a guide for researchers in choosing optimal discretization methods.