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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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Adapting Random Forests to Predict Obesity-Associated Gene Expression.

Jeremy Watts, Elexis Allen, Ahmad Mitoubsi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
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    Summary

    Random forests effectively predict gene expression from genotype data. This study explores encoding and balancing methods for predicting obesity-associated gene expression, outperforming other methods in specific cases.

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    Area of Science:

    • Genomics and Bioinformatics
    • Systems Biology
    • Obesity Research

    Background:

    • Predicting gene expression from genotype data is crucial for understanding biological mechanisms.
    • Random Forests (RFs) are powerful machine learning models for such predictions.
    • The impact of feature encoding and data balancing on RF performance in gene expression prediction remains under-explored.

    Purpose of the Study:

    • To compare Random Forest (RF) regressors and classifiers for gene expression prediction.
    • To investigate the influence of ordinal versus one-hot encoding and data balancing on RF performance.
    • To evaluate RFs for predicting obesity-associated gene expression in subcutaneous adipose tissue.

    Main Methods:

    • Utilized Random Forest (RF) algorithms, comparing both regression and classification approaches.
    • Examined the effects of different feature encoding strategies (ordinal and one-hot encoding).
    • Assessed the role of data balancing techniques, specifically oversampling, in model training.
    • Compared RF performance against PrediXcan for predicting obesity-associated gene expression.

    Main Results:

    • Random Forests (RFs) demonstrate competitive performance against PrediXcan in predicting obesity-associated gene expression in subcutaneous adipose tissue.
    • RFs successfully generated predictions for genes where PrediXcan encountered failures.
    • The study provides insights into the optimal encoding and balancing strategies for RF-based gene expression prediction.

    Conclusions:

    • Random Forests offer a robust alternative for predicting gene expression from genotype data, particularly for complex traits like obesity.
    • RFs show promise in identifying relevant gene expression patterns, even in challenging datasets or when other methods falter.
    • Further research into feature engineering and model optimization for RFs in genomics is warranted.