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The Probabilistic Genotyping Software STR mix: Utility and Evidence for its Validity
Author(s) -
Buckleton John S.,
Bright JoAnne,
Gittelson Simone,
Moretti Tamyra R.,
Onorato Anthony J.,
Bieber Frederick R.,
Budowle Bruce,
Taylor Duncan A.
Publication year - 2019
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.13898
Subject(s) - probabilistic logic , computer science , coding (social sciences) , software , software quality , data mining , interpretation (philosophy) , reliability (semiconductor) , binary data , binary number , data science , statistics , mathematics , artificial intelligence , software development , power (physics) , physics , arithmetic , quantum mechanics , programming language
Abstract Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision‐making toward computer‐based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping ( PG ). Decision‐making by laboratories to implement probability‐based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STR mix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio ( LR ) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG , and in particular, STR mix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STR mix™, one of the available PG software.