Deep Learning-Based STR Analysis for Missing Person Identification in Mass Casualty Incidents.

Deoxyribonucleic acid (DNA) profiling is an important branch of forensic science that aids in the identification of missing people, particularly in mass disasters. This study presents an artificial intelligence system that utilizes DNA-Short Tandem Repeat (STR) data to identify victims using Deep Neural Network (DNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (Bi-GRU) deep learning models. The identification of STR information for living family members, such as parents or brothers, poses a significant challenge in victim identification. Familial data are artificially generated based on the actual data of distinct Iraqi individuals from the province of Al-Najaf. Two people are selected as male and female to create a family of 10 members. As a result of this action, 151,580 individuals were generated from 106 different people, which helps to overcome the lack of datasets caused by restrictive policies and the confidentiality of familial datasets in Iraq. These datasets are prepared and formatted for training deep learning models. Based on various reference datasets, the models are built to handle five different scenarios where both parents are alive, only one parent is alive, or the siblings are available for reference. The three models' performances were compared: Bi-GRU performed the best, with a loss of 0.0063 and an accuracy of 0.9979, followed by GRU with a loss of 0.0102 and an accuracy of 0.9964, and DNN with a loss of 0.2276 and an accuracy of 0.9174. The evaluation makes use of a confusion matrix and receiver operating characteristic curve. Based on the literature, this is the first attempt to introduce deep learning in DNA profiling, which reduces both time and effort. Despite the fact that the proposed deep learning models have good results in identifying missing persons according to their families, these models have limitations that can be confined to the availability of familial DNA profiles. The system doesn't work well if no relative samples are available as references, such as a father, mother, or brother. In the future, DNN, GRU, and Bi-GRU models will be applied to mini-STR sequences that are used in cases of degraded victims of incomplete STR sequences. [ABSTRACT FROM AUTHOR]