Bioinformatics and Artificial Intelligence in Forensic Microbiology

Forensic microbiology stands as a transformative field that utilises microbial signatures to unravel intricate criminal cases, track contamination events, and identify potential biothreat agents. The surge of high-throughput sequencing has vastly expanded the complexity and scale of microbial data, requiring sophisticated computational techniques for effective analysis. Bioinformatics emerges as an essential discipline by providing pipelines and algorithms to process and compare metagenomic data derived from diverse forensic samples. Simultaneously, artificial intelligence (AI), incorporating machine learning and deep learning methodologies, facilitates pattern recognition, anomaly detection, and predictive modelling at large scales. By integrating these advanced approaches, practitioners can achieve precise source attribution, post-mortem interval estimation, and contamination tracing, thereby revolutionizing forensic investigations and advancing public safety.This Research Topic aims to explore the fruitful intersection of bioinformatics, AI, and forensic microbiology. We encourage submissions of original research, methods, case studies, reviews, and perspectives that focus on algorithm and pipeline development for forensic metagenomics, AI-driven microbial community analysis, and advancements in microbial source tracking. Interest also lies in predictive modelling for forensic timelines and novel strategies for data integration. Contributions that delve into the ethical, legal, and social implications of AI in forensic contexts, as well as challenges such as data standardization, privacy concerns, and reproducibility, are highly sought. Interdisciplinary studies that combine computational and experimental practices, striving to establish best practices and benchmark datasets, are particularly welcome.To gather further insights into the application of bioinformatics and AI in forensic microbiology, we welcome articles addressing, but not limited to, the following themes:o Algorithm and pipeline development for forensic metagenomicso AI-driven microbial community analysiso Advances in microbial source trackingo Predictive modelling for forensic timelineso Novel data integration strategiesMoreover, discussions on ethical, legal, and social implications or addressing challenges of utilizing AI in forensic contexts are encouraged. Through encouraging cross-disciplinary dialogue and sharing innovative tools, this collection aims to chart future pathways and establish best practices at the confluence of microbiome science, forensics, and AI. Topic editors: Kanchan Yadav, Vijay Nema, Rahul Shivahare Source: https://www.frontiersin.org/research-topics/72872/bioinformatics-and-artificial-intelligence-in-forensic-microbiology