Molecular and Computational Innovations in Forensic Genetics: From Degraded DNA to Predictive Models
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Genetics and Genomics".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 3
Special Issue Editors
Interests: chemometrics; multivariate data analysis; forensic toxicology; validation; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: forensic molecular anthropology; human remains identification; DNA; STRs; SNPs; phenotyping; biogeographical ancestry; massively parallel sequencing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue will explore the convergence of molecular and computational innovations in forensic genetics, with a particular focus on two key areas: the analysis of degraded DNA and the application of machine learning and chemometric approaches. Contributions may address either of these areas individually or examine their integration, with the shared goal of advancing forensic genetic investigations through methodological innovation and interdisciplinary collaboration.
The Issue aims to provide a comprehensive overview that bridges theoretical advances and practical applications, showcasing how both molecular and computational tools can support forensic investigations—particularly in challenging contexts involving low-quality or fragmented DNA. Emphasis will be placed on the synergy between molecular biology, data science, and forensic practice, with the goal of fostering innovation and dialogue across disciplines.
Key topics will include the following:
- Data fusion and multivariate integration: strategies to combine genetic, biochemical, and trace evidence data; approaches for handling heterogeneous and degraded datasets to extract complementary information.
- Machine learning and deep learning models: applications of supervised and unsupervised learning in forensic genetics; emerging use of deep neural networks for complex or high-dimensional data, including degraded DNA profiles.
- Innovations in degraded DNA analysis: novel molecular techniques for analyzing and interpreting compromised genetic material; exploration of new substrates and sample types; optimization of workflows for low-template or fragmented DNA.
- Explainability, transparency, and validation: ensuring model interpretability to meet forensic and legal standards; statistical validation frameworks to assess robustness and reproducibility, particularly when working with compromised samples.
- Applied perspectives and case studies: real-world examples where AI-based methods enhanced forensic genetic investigations; discussion of opportunities and limitations for routine adoption.
- Future outlook: the role of interdisciplinary collaboration among geneticists, data science, and forensic stakeholders; ethical and regulatory considerations surrounding the use of AI for forensic genetics.
By welcoming contributions from both molecular and computational perspectives, this Special Issue aims to highlight the transformative potential of innovation in forensic genetics—whether through improved analysis of degraded DNA, the adoption of predictive models, or the integration of these complementary approaches.
Dr. Eugenio Alladio
Dr. Elena Pilli
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Keywords
- degraded DNA
- low-template DNA
- molecular forensics
- NGS
- machine learning
- deep learning
- chemometrics
- data integration
- predictive models
- forensic applications
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