Validation of a neural network approach for STR typing to replace human reading

Machine learning applications in forensic DNA profiling: A critical review

Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.

A Primer on Chimerism Analysis: A Straightforward, Thorough Review

Short tandem repeat (STR) analysis to assess chimerism is a critical aspect of routine care particularly in patients facing stem cell transplants but is also relevant in other clinical scenarios. STR analysis provides a means to assess donor and recipient cellular origins in a patient, and, as such, can inform engraftment, rejection, and relapse status in stem cell transplant recipients. In this review of STR testing, the most commonly used method to assess chimerism, its background, procedural details, and clinical utility are discussed.