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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Wound age estimation based on next-generation sequencing: Fitting the optimal index system using machine learning.

Kang Ren1, Liangliang Wang1, Yifei Wang1

  • 1School of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030606, Shanxi, P.R. China.

Forensic Science International. Genetics
|May 31, 2022
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Summary
This summary is machine-generated.

Estimating wound age is crucial for forensic investigations. This study developed a novel method using gene expression profiles and machine learning, achieving 85.71% accuracy in determining wound age.

Keywords:
Forensic pathologyIndex systemMachine learning algorithmsNext-generation sequencingSkeletal muscle contusionWound age estimation

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Area of Science:

  • Forensic Medicine
  • Genomics
  • Bioinformatics

Background:

  • Accurate wound age estimation is vital in forensic investigations but remains challenging.
  • Objective biological indicators for wound age are needed.
  • Next-generation sequencing (NGS) offers potential for developing such indicators.

Purpose of the Study:

  • To explore an objective method for wound age estimation using NGS gene expression profiles.
  • To identify reliable gene expression patterns indicative of wound age.
  • To evaluate machine learning algorithms for wound age classification.

Main Methods:

  • Utilized Sprague-Dawley rats, inducing contusions and collecting samples at various time points (4-48 hours).
  • Employed NGS to identify differentially expressed genes (Set A) and hub genes (Sets B, C, D).
  • Applied four machine learning algorithms (logistic regression, SVM, MLP, random forest) to classify wound age.

Main Results:

  • The combination of Set A genes and the random forest algorithm demonstrated the highest efficiency.
  • External validation achieved an accuracy of 85.71%, with only one misclassification.
  • This approach highlights the potential of NGS and bioinformatics for wound age estimation.

Conclusions:

  • NGS-based gene expression profiling combined with machine learning provides a promising tool for objective wound age estimation.
  • The developed index system shows significant potential for forensic applications.
  • Further research can refine this method for improved accuracy and broader applicability.