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

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Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer:

Solbi Kweon^1,2, Jeong Hoon Lee¹, Younghee Lee³

¹Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.

Journal of Medical Internet Research

|August 11, 2020

Summary

Genomic data can predict some patient information like gender and cancer type, but not age or race. RNA expression data alone has limited ability to identify specific patients, ensuring genomic data privacy.

Keywords:

RNA sequencing cancer machine learning personal information prediction privacy issue

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Area of Science:

Genomics and Bioinformatics
Machine Learning in Healthcare
Biostatistics

Background:

Growing need for genomic data sharing raises privacy and ethical concerns.
Disclosure of personal information from genomic data is a significant issue.

Purpose of the Study:

To determine if genomic data is sufficient for predicting patient personal information.
To assess the predictability of personal variables from RNA expression data.

Main Methods:

Utilized RNA expression data from 9538 patients in The Cancer Genome Atlas program.
Applied four machine learning algorithms (SVM, Decision Tree, Random Forest, ANN) to predict five personal variables (age, gender, race, cancer type, stage).
Evaluated model performance using accuracy and AUC, testing on five cancer types and normal samples.

Main Results:

Gender and cancer type were highly predictable (accuracies 0.93-0.99 and 0.78-0.94, respectively).
Age and race were difficult to predict (accuracies 0.0026-0.29 and 0.76-0.96).
Support vector machine achieved the highest mean accuracy (0.77), while random forest had the lowest (0.65).

Conclusions:

RNA expression data allows prediction of certain patient identifiers but not all.
The predictive power of RNA expression data for personal information is limited.
This study suggests genomic data alone is insufficient for identifying specific patients, supporting data privacy.