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Bayesian network tool for analyzing the cost-effectiveness of bulk forensic trace DNA profiling.

Tuomas Korpinsalo1,2, Markus Pirttimaa2, Tapani Reinikainen2

  • 1University of Helsinki, Helsinki, Finland.

Journal of Forensic Sciences
|April 30, 2026
PubMed
Summary

Forensic DNA profiling faces challenges with bulk sample analysis. A Bayesian network model suggests filtering samples by type and DNA quantity can improve cost-effectiveness and efficiency in forensic laboratories.

Keywords:
Bayesian networksDNA profilingcost–benefit analysisdata analysisdecision‐makingforensic statistics

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Area of Science:

  • Forensic Science
  • Molecular Biology
  • Bioinformatics

Background:

  • DNA profiling has transformed forensic investigations.
  • Increased demand and scalable technology present challenges for forensic laboratories managing bulk sample analysis.
  • Efficient management of forensic DNA processes is crucial for timely investigations.

Purpose of the Study:

  • To develop a Bayesian network for quantitative decision-making in forensic DNA laboratories.
  • To analyze the cost-effectiveness of bulk trace DNA profiling.
  • To identify strategies for improving the efficiency of forensic DNA profiling workflows.

Main Methods:

  • Development of a Bayesian network model for decision-making.
  • Analysis of over 21,000 DNA sample results from the National Bureau of Investigation Forensic Laboratory (NBI-FL) in Finland.
  • Evaluation of sample type and extracted DNA quantity for cost-effectiveness.

Main Results:

  • Filtering samples based on type and DNA quantity significantly reduces processed volumes with minimal risk of missing usable profiles.
  • Trace DNA samples, particularly touch samples or those with low DNA quantity, have a low probability of yielding usable profiles.
  • Processing low-yield DNA samples is unlikely to be cost-effective in routine forensic workflows.

Conclusions:

  • Redesigning forensic sample processing protocols to prioritize certain sample types and quantities can enhance DNA profiling efficiency.
  • The developed Bayesian network model offers a template for other forensic laboratories to optimize their workflows.
  • Implementing data-driven filtering strategies can lead to substantial cost savings and improved investigative outcomes.