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开发一种用于体液识别的解释模型

Courtney R H Lynch¹, Rachel Fleming², James M Curran³

¹Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, New Zealand; School of Chemistry, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.

Forensic science international

|April 30, 2024

概括

机器学习模型,包括多项逻辑回归,使用实时定量逆转录PCR (RT-qPCR) 从信使RNA (mRNA) 档案准确识别体液类型. 这些先进的方法改善了法医分析,特别是对于具有挑战性的样本,如月经液.

科学领域:

法医科学法医科学法医科学
分子生物学分子生物学
机器学习是机器学习.

背景情况:

刑事调查,特别是性侵犯,需要身体液体识别的背景.
传统的方法很难识别某些流体,如月经和阴道材料.
终点逆转录PCR (RT-PCR) 用于检测体液特定的信使RNA (mRNA).

研究的目的:

应用和评估机器学习模型用于体液识别,使用来自实时定量逆转录PCR (RT-qPCR) 的单源mRNA配置文件.
为了比较多项逻辑回归 (MLR),天真贝叶斯 (NB) 和线性差异分析 (LDA) 在分类体液中的性能.
评估定量值周期 (Cq) 值与分类中的存在/缺失数据的实用性.

主要方法:

用RT-qPCR从唾液,血液,月经液,阴道物质和精液中获得mRNA概况.
机器学习模型包括MLR,NB和LDA被应用来分类体液类型.
用接收器操作特征 (ROC) 曲线来分析按体液类型的分类性能.

主要成果:

多项逻辑回归 (MLR) 显示了最高的性能,在定量Cq值下达到0.95的总准确度.
从RT-qPCR获得的定量Cq数据与存在/缺席数据相比,提供了更好的分类准确性.
所有测试的方法都在准确分类月经血液样本方面表现出挑战.

结论:

机器学习模型,特别是MLR,对于从RT-qPCRmRNA配置文件中对体液进行分类是有效的.
定量RT-qPCR数据显著有助于准确的体液识别.
需要进一步的研究来解决月经液的分类困难,并模拟体液混合物.

关键词:

身体液体的识别身体液体的识别在 MRNA 分析中,使用 MRNA 进行分析.概率解释方法的可能解释方法.定量PCR是一种定量PCR.

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