Machine learning applications in forensic DNA profiling: A critical review.

Area of Science:

• Computational Biology
• Forensic Science
• Genetics

Background:

• Machine learning (ML) algorithms offer advanced computational power for analyzing large datasets.
• ML applications are widespread, now extending into forensic science, including DNA analysis.
• Traditional manual analysis of forensic DNA data is often complex, time-consuming, and prone to errors.

Purpose of the Study:

• To introduce machine learning (ML) methods and their potential applications in forensic DNA analysis.
• To critically review the current literature on ML in forensic DNA analysis.
• To bridge the knowledge gap between ML professionals and the forensic science community.

Main Methods:

• Review of existing literature on machine learning applications in forensic science.
• Introduction to fundamental machine learning concepts and algorithms.
• Discussion of the specific requirements and challenges within forensic DNA analysis.

Main Results:

• Machine learning offers potential for streamlining forensic DNA data analysis, enhancing accuracy and reproducibility.
• Current literature highlights the novelty and growing interest in ML for forensic DNA applications.
• Significant knowledge gaps exist between ML experts and forensic practitioners.

Conclusions:

• Machine learning presents a promising avenue for advancing forensic DNA analysis techniques.
• Further interdisciplinary collaboration is needed to effectively integrate ML into forensic workflows.
• Addressing the current limitations and knowledge gaps will be crucial for successful ML adoption in forensic science.