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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Minimum standards for evaluating machine-learned models of high-dimensional data.

Brian H Chen^1,2

¹FOXO Technologies Inc, Minneapolis, MN, United States.

Frontiers in Aging

|September 30, 2022

Summary

This summary is machine-generated.

Rigorous validation is crucial for machine learning predictive models, especially with high-dimensional data. Independent validation of algorithmic biomarkers ensures reliable model selection and scientific reproducibility.

Keywords:

DNA methylation aging epigenetic clock epigenetics machine learning omics reproducibility validation

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Area of Science:

Biomedical informatics
Machine learning
Genomics

Background:

Machine learning models, including epigenetic clocks, are increasingly used for prediction with high-dimensional data.
High-dimensional data (large p, small n) presents challenges like overfitting for these models.

Purpose of the Study:

To advocate for mandatory independent validation of algorithmic biomarkers.
To enhance the reliability and reproducibility of machine learning models in science.

Main Methods:

The study emphasizes the need for independent validation protocols.
It highlights the importance of reproducibility in machine learning model development.

Main Results:

Overfitting is a significant risk in machine learning models trained on limited, high-dimensional datasets.
Independent validation can efficiently identify robust prediction and classification models.

Conclusions:

Implementing independent validation is essential for the credible development of algorithmic biomarkers.
Greater scientific rigor is needed for newly developed predictive and classification models.