ArticleInternational journal of legal medicine2023
Selection and validation of a novel set of specific differential methylation markers and construction of a random forest prediction model for the accurate tissue origin identifications of body fluids involving young and middle-aged group of Chinese Han population.
Article in International journal of legal medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Recent advances in the identification of vaginal fluid for forensic applications.International journal of legal medicine · 2026Review
- A novel hybrid model to predict concomitant diseases for Hashimoto's thyroiditis.BMC bioinformatics · 2023Article
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5 authors.
Funding
Abstract
The identification of tissue origin of body fluid is helpful to the determination of the case nature and the reproduction of the case process. It has been confirmed that tissue-specific differential methylation markers could be used to identify the tissue origins of different body fluids. To select suitable tissue-specific differential methylation markers and establish the efficient typing system which could be applied to the identifications of body fluids in forensic cases involving Chinese Han individuals of young and middle-aged group, a total of 125 body fluids (venous blood, semen, vaginal fluid, saliva, and menstrual blood) were collected from healthy Chinese Han volunteers aged 20-45 years old. After genome-wide explorations of DNA methylation patterns in these five kinds of body fluids based on the Illumina Infinium Methylation EPIC BeadChip, 15 novel body fluid-specific differential CpGs were selected and verified based on the pyrosequencing method. And these identification efficiencies for target body fluids were verified by ROC curves. The pyrosequencing results indicated that the average methylation rates of nine CpGs were consistent with those of DNA methylation chip detection results, and the other five CpGs (except for cg12152558) were still helpful for the tissue origin identifications of target body fluids. Finally, a random forest classification prediction model based on these 14 CpGs was constructed to successfully identify five kinds of body fluids, and the tested accuracy rates all reached 100%.
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