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Explainable artificial intelligence in forensic DNA analysis: Alleles identification in challenging electropherograms

Mengyu Tan¹, Yuxuan Tan¹, Haoyan Jiang¹

¹Department of Forensic Genetics, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China.

Forensic Science International. Genetics

|April 27, 2025

Summary

This summary is machine-generated.

Machine learning models improve capillary electrophoresis analysis of challenging DNA samples by distinguishing true alleles from artefacts. This enhances data interpretation in forensic science, though mixture samples require further optimization.

Keywords:

Alleles identification Capillary electrophoresis Electropherogram Machine learning Short tandem repeat analysis

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Area of Science:

Forensic Science
Genetics
Computational Biology

Background:

Capillary electrophoresis (CE) short tandem repeat (STR) analysis faces challenges with artefactual signals in complex DNA samples.
Existing electropherogram (EPG) reading systems struggle to completely filter these artefacts, complicating allele interpretation.
Artificial intelligence (AI) shows promise in differentiating true allele signals from artefacts in EPGs.

Purpose of the Study:

To evaluate the effectiveness of traditional machine learning algorithms in classifying EPG signals for improved STR analysis.
To develop and validate AI-based models for distinguishing allele signals from artefacts in challenging forensic samples.
To create a user-friendly platform for automated EPG signal classification.

Main Methods:

Five traditional machine learning algorithms were trained on EPG signal datasets (single-source, low-template, and mixture samples).
Models were evaluated and validated using independent datasets.
Receiver Operating Characteristic (ROC) curve analysis and prediction probability thresholds were implemented.
An ensemble learning platform was developed for signal classification.

Main Results:

Machine learning models demonstrated feasibility in improving the reportability of potential information from EPGs.
False positive classifications were significantly reduced using ROC analysis and probability thresholds.
Performance for mixture EPGs requires further optimization for enhanced classification accuracy.
The developed platform integrates multiple models for robust EPG signal classification.

Conclusions:

Machine learning-based EPG signal classification significantly enhances the efficiency and accuracy of DNA sample analysis and interpretation.
The developed platform offers a more optimal and robust solution for forensic analysts.
Further research and optimization are warranted, particularly for complex mixture samples.