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Multi-omics integration strategy in the post-mortem interval of forensic science.

Jian Li1, Yan-Juan Wu1, Ming-Feng Liu1

  • 1School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, PR China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China.

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Forensic scientists can now estimate the post-mortem interval (PMI) more accurately using a new multi-omics stacking model (MOSM). This AI-driven approach integrates various molecular data for improved PMI prediction in legal investigations.

Keywords:
Machine learningMulti-omicsPost-mortem intervalStacking algorithm

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Area of Science:

  • Forensic Science
  • Computational Biology
  • Biochemistry

Background:

  • Accurate post-mortem interval (PMI) estimation is crucial for forensic investigations.
  • Current PMI estimation methods based on molecular markers have limitations in accuracy and comprehensiveness.
  • There is a need for advanced techniques to improve the reliability of PMI determination.

Purpose of the Study:

  • To develop and validate an innovative approach for accurate post-mortem interval (PMI) estimation.
  • To integrate multi-omics data with artificial intelligence for enhanced PMI prediction.
  • To address the limitations of existing methods in forensic science.

Main Methods:

  • Development of the multi-omics stacking model (MOSM), integrating metabolomics, protein microarray electrophoresis, and Fourier Transform-Infrared Spectroscopy data.
  • Application of machine learning models with distinct algorithmic principles within the MOSM framework.
  • Utilizing multimolecular, multimarker, and multidimensional information for biological process description.

Main Results:

  • The MOSM achieved a prediction accuracy of 0.93 for PMI.
  • Demonstrated a generalized area under the receiver operating characteristic curve (AUC) of 0.98.
  • Reported a minimum mean absolute error (MAE) of 0.07, indicating high precision.

Conclusions:

  • The multi-omics stacking model (MOSM) significantly improves the accuracy and robustness of post-mortem interval estimation.
  • Integration of diverse omics data and machine learning enhances the generalizability of PMI predictions.
  • This AI-driven approach offers a more comprehensive and reliable tool for forensic applications.