2020
DOI: 10.1016/j.fsigen.2020.102300
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Age estimation using bloodstain miRNAs based on massive parallel sequencing and machine learning: A pilot study

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Cited by 19 publications
(13 citation statements)
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“…Therefore, we stated that RF could be viewed as the preferable machine learning method for age prediction in this study. In comparison to previous studies (Zubakov et al, 2016;Fang et al, 2020;Wang et al, 2022), relatively low MAE and RMSE between actual and predicted results were observed in the current study. We postulated that these results might be related to a small age bracket (18-41), which leads to low MAE and RMSE.…”
Section: Development Of Age Prediction Models By Three Machine Methodscontrasting
confidence: 85%
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“…Therefore, we stated that RF could be viewed as the preferable machine learning method for age prediction in this study. In comparison to previous studies (Zubakov et al, 2016;Fang et al, 2020;Wang et al, 2022), relatively low MAE and RMSE between actual and predicted results were observed in the current study. We postulated that these results might be related to a small age bracket (18-41), which leads to low MAE and RMSE.…”
Section: Development Of Age Prediction Models By Three Machine Methodscontrasting
confidence: 85%
“…Furthermore, nine mRNAs displayed negative correlation with age, and their correlation coefficient ranged from -0.47 to -0.28. Compared to six age-associated miRNAs selected by Fang et al (2020), the 14 mRNAs presented in this study showed higher correlation with age, implying that these 14 mRNAs might be more beneficial for age estimations.…”
Section: Selection Of Age-associated Mrnasmentioning
confidence: 63%
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“…Therefore, some forensic scientists proposed the potential forensic application of miRNAs in estimating the age of a donor of biological samples. Fang et al (Fang et al, 2020) established age prediction models for bloodstains based on six age-related miRNAs (miR-98-3p, miR-324-3p, miR-32-3p, miR-330-5p, miR-374c-5p and miR-342-3p) using seven machine learning models. Results showed that the mean absolute error (MAE) ranged from 6.56 to 9.262 years.…”
Section: Introductionmentioning
confidence: 99%