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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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A survey on single and multi omics data mining methods in cancer data classification.

Zahra Momeni¹, Esmail Hassanzadeh¹, Mohammad Saniee Abadeh²

¹Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.

Journal of Biomedical Informatics

|June 12, 2020

Summary

This study reviews challenges in analyzing cancer data using single and multi-omics approaches. It highlights gene selection and data integration as key areas for improving biomedical research reliability.

Keywords:

Cancer classification Data integration Gene selection High dimensional datasets Single and multi omics data

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Area of Science:

Biomedical research
Bioinformatics
Data Science

Background:

Data analytics and bioinformatics are crucial for understanding cancer's molecular aspects.
Single-omics studies, often using gene expression data, face machine learning challenges like low sample counts and high attribute numbers.
Single-omics analyses can provide limited insights into complex disease mechanisms.

Purpose of the Study:

To provide a comprehensive overview of challenges in single and multi-omics cancer data analysis.
To focus on critical areas of gene selection and data integration.
To guide future research towards more reliable cancer data analysis.

Main Methods:

Review of existing literature on single and multi-omics data analysis in cancer research.
Identification and discussion of challenges in gene selection for single-omics data.
Exploration of data integration methods for multi-omics cancer data.

Main Results:

Single-omics data analysis presents significant challenges due to the 'small-n large-p' problem, impacting gene selection reliability.
Multi-omics data integration is essential for a more comprehensive understanding of cancer's molecular landscape.
Current methods for data integration are a major hurdle in advancing multi-omics cancer research.

Conclusions:

Addressing gene selection and data integration challenges is vital for enhancing the reliability of cancer data analysis.
Multi-omics approaches hold promise for more robust insights into disease mechanisms.
Further development in data integration techniques is needed to fully leverage multi-omics data in cancer research.