# Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives
## 4 Discussion
It may be possible to identify new types of functional elements by using chromatin data to annotate the genome in an \"unsupervised\" manner [40]. In order to comprehend the mechanics underlying gene expression, ML techniques have also been applied. While some methods take into account TF binding profiles at the gene promoter region or ChIP-seq histone modification, others predict gene expression only based on the DNA sequence. By developing a network model, more advanced techniques try to jointly model the expression of every gene in a cell [66]. ML researchers have a history of concentrating on a subset of statistical problems and placing a strong emphasis on the analysis of big, diverse data sets. The ML literature generally lacks basic statistical notions like power calculations, statistical confidence estimation, and likelihood estimate calibration [67]. Due to their complexity and diversity, the detection of genetic markers employed for forensic DNA typing necessitates a substantial amount of data, which may make manual implementation challenging [68]. In addition, handling such data, interpreting it, and comparing it by hand is difficult, labor-intensive, and prone to inaccuracy, despite it occurring rarely [69]. Therefore, analyzing and interpreting such a large amount of data may be made easier by artificial intelligence, which has access to bioinformatic techniques, mathematical algorithms, and statistical computations. Based on the data obtained through this literature review, to date the application of AI in the field of the forensic genetics is limited to two main fields: haplogroup classification and STR electropherogram interpretation. From a practical standpoint, AI can expedite the forensic investigation process, helping forensic geneticists and law enforcement agencies identify human remains or unknown individuals more efficiently. This is particularly relevant in cases involving missing persons or mass disasters, where the identification process can be challenging, considering the great amount of data that could be handled. The application of AI algorithms in haplogroup classification can streamline and improve the accuracy of this process, benefiting forensic investigations and scientific research alike. With further advancements and continued collaboration between these fields, AI can revolutionize the way forensic genetics and haplogroup classification are conducted, enabling faster and more accurate analyses of genetic data [5]. In the same manner, AI has transformed the field of forensic genetics, especially in the interpretation of STRs. The interpretation of DNA profiling is related to the peak height intensities expressed in relative fluorescence units (RFU), which are shown on an electropherogram (EPG), a graph of fluorescence vs. time. Allelic peak heights for a perfect PCR process are correlated with the amounts that come from the contributing individuals. These peak heights, however, are random and will differ between PCR procedures, particularly in cases where there is little DNA or when there is degradation or allelic drop-out--a result of low template and/or damaged DNA that leads to incomplete DNA profiles, stutter, or artifacts [70,71]. Due to these factors, forensic genetic analyses may complicate the manual interpretation of mixed DNA profiles, and STRs in particular may be amplified and then sequenced [72]. These restrictions are removed and the full potential of the DNA typing data is realized by statistical software packages that integrate probabilistic interpretation models [73,74]. Fully continuous DNA mixture interpretation software, such as STRmixTM, has been extensively tested and used in forensic DNA labs to analyze STR data [75]. STRmixTM determines the probability of the profile given all potential genotype combinations by utilizing quantitative data from an EPG, such as peak heights. We give the normalized probability density a number, or weight. The likelihood of the EPG given each potential genotype combination at a locus is given a relative weight by STRmixTM [70].