Forensic STR allele extraction using a machine learning paradigm

Liu, Yao-Yuan; Welch, David; England, Ryan; Stacey, Janet; Harbison, SallyAnn

doi:10.1016/j.fsigen.2019.102194

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Machine learning could build a high-efficient and accurate predicted model for various purposes, which has shown great promising aspects in clinical and forensic research (Obermeyer and Emanuel, 2016;Liu et al, 2020;Peña-Solórzano et al, 2020;Santolaria, 2021). For RF, it is an ensemble learning algorithm by developing a number of decision trees.…”

Section: Development Of Age Prediction Models By Three Machine Methodsmentioning

confidence: 99%

Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods

et al. 2022

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Aging is usually accompanied by the decline of physiological function and dysfunction of cellular processes. Genetic markers related to aging not only reveal the biological mechanism of aging but also provide age information in forensic research. In this study, we aimed to screen age-associated mRNAs based on the previously reported genome-wide expression data. In addition, predicted models for age estimations were built by three machine learning methods. We identified 283 differentially expressed mRNAs between two groups with different age ranges. Nine mRNAs out of 283 mRNAs showed different expression patterns between smokers and non-smokers and were eliminated from the following analysis. Age-associated mRNAs were further screened from the remaining mRNAs by the cross-validation error analysis of random forest. Finally, 14 mRNAs were chosen to build the model for age predictions. These 14 mRNAs showed relatively high correlations with age. Furthermore, we found that random forest showed the optimal performance for age prediction in comparison to the generalized linear model and support vector machine. To sum up, the 14 age-associated mRNAs identified in this study could be viewed as valuable markers for age estimations and studying the aging process.

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Section: Development Of Age Prediction Models By Three Machine Methodsmentioning

confidence: 99%

Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods

et al. 2022

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“…Efforts are underway to understand and model instrumental artifacts [ [248] , [249] , [250] , [251] ] as well as biological artifacts of the PCR amplification process such as STR stutter products [ 252 , 253 ]. Machine learning approaches are being applied to classify artifacts versus alleles with the goal to eventually replace manual data interpretation with computer algorithms [ [254] , [255] , [256] , [257] ]. One such program, FaSTR DNA, enables potential artifact peaks from stutter, pull-up, and spikes to be filtered or flagged, and a developmental validation has been published examining 3403 profiles generated with seven different STR kits [ 258 ].…”

Section: Advancements In Current Practicesmentioning

confidence: 99%

Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019–2022

Butler

2023

Forensic Science International: Synergy

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“…Machine Learning is a collection of statistical analysis methods which help to solve different types of problems such as classification, clustering, regression, etc. The methods of ML are being used in different fields of forensic examination [24][25][26], especially in DNA [27,28], fingerprints [29][30][31][32], handwriting [33,34], footwear [35], acoustics [36,37], etc. Also, several attempts were made to apply Machine Learning to firearms identification [38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Examination of the possibility to use Siamese networks for the comparison of firing pin marks

Giverts¹,

Sorokina

Fedorenko

2022

Journal of Forensic Sciences

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One of the most discussed issues in forensic firearms identification is the subjectivity of conclusions. The main part of firearms examiners' work is to make a microscopic comparison of the marks on cartridge cases and bullets. In this process, examiners have to decide if the quantity and the quality of the observed characteristics are sufficient for identification. This decision is based on the personal experience of an examiner, so examiners with different backgrounds can come to different conclusions, and this fact presents a problem. Besides, the calculation of the error rate for this type of examination is a debatable issue. Different mathematical and statistical models were proposed, and computer-based algorithms were developed in order to avoid subjectivity and to determine error rates. This article investigates the possibility to use methods of machine learning for the comparison of marks of the firing pin impressions on cartridge cases. In the research, the Siamese network model, which included two similar Convolutional Neural Networks, was prepared and trained. For the training and validation of the model, the database of firing pin impressions was prepared. This database included images of cartridge cases discharged from 300 firearms that came from regular casework and clone images used for data augmentation. The model was trained and examined using the validation part of the database. The metrics, such as accuracy, sensitivity, and specificity were calculated. The results of the research show the possibility of using the Siamese network for building an objective forensic firearms examination system with a known error rate.

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Forensic STR allele extraction using a machine learning paradigm

Cited by 6 publications

References 22 publications

Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods

Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods

Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019–2022

Examination of the possibility to use Siamese networks for the comparison of firing pin marks

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