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Investigación de vangarda para o desenvolvemento sostible

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2026

Rodriguez, Jose L.; Ascencio, Estefania; Ageitos, Jose M.; Feijoo, Lucia; He, Shan; Daghighi, Amirreza; Casanola-Martin, Gerardo M.; Munteanu, Cristian R.; Rodríguez-Yáñez, Santiago; Pazos, Alejandro; Bediaga, Harbil; Ordiales, Brenda Durand; Esteban, Raquel; Heres, Ana-Maria; Yuste, Jorge Curiel; Epelde, Lur; Rasulev, Bakhtiyor; Gonzalez, Tomas; Arrasate, Sonia; González-Diaz, Humberto

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence-Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study Journal Article

In: Advanced Intelligent Systems, vol. 8, no. 4, pp. e202500587, 2026, ISSN: 2640-4567.

Abstract | Links | BibTeX | Etiquetas: DNA sequences, machine learning, Perturbation Theory, Shannon Information Theory

@article{rodriguez_polymerase_2026,

title = {Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence-Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study},

author = {Jose L. Rodriguez and Estefania Ascencio and Jose M. Ageitos and Lucia Feijoo and Shan He and Amirreza Daghighi and Gerardo M. Casanola-Martin and Cristian R. Munteanu and Santiago Rodríguez-Yáñez and Alejandro Pazos and Harbil Bediaga and Brenda Durand Ordiales and Raquel Esteban and Ana-Maria Heres and Jorge Curiel Yuste and Lur Epelde and Bakhtiyor Rasulev and Tomas Gonzalez and Sonia Arrasate and Humberto González-Diaz},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202500587},

doi = {10.1002/aisy.202500587},

issn = {2640-4567},

year  = {2026},

date = {2026-01-01},

urldate = {2026-08-18},

journal = {Advanced Intelligent Systems},

volume = {8},

number = {4},

pages = {e202500587},

abstract = {The exponential growth of genomic data has created a pressing need for methods capable of interpreting complex biological information, especially in fields like paleogenomics and environmental metagenomics. Ancient DNA (aDNA) analysis faces challenges such as degradation and contamination, while soil metagenomic DNA (soDNA) analysis is hindered by microbial diversity and incomplete reference databases. To address these limitations, this study proposes the polymerase chain reaction (PCR).Perturbation Theory and Machine Learning (PTML) methodology, which integrates machine learning with Perturbation Theory to analyze genetic sequences without the need for alignment. Two models are developed: the first classifies bacterial aDNA sequences extracted from Miocene amber; the second predicts tree health using microbial gene abundance in forest soils. Both rely on entropy-based descriptors (θk) and structural differences (Δθk) between query and reference sequences, which serve as perturbation operators for supervised learning algorithms. This approach allows the detection of meaningful patterns even without complete genomic references. The aDNA model achieves 99.65% sensitivity and 99.81% specificity, while the soDNA model reaches 98.85% sensitivity and 92.56% specificity. These results confirm the robustness and applicability of PCR.PTML in diverse genomic contexts, presenting it as a valuable tool for ancient DNA classification and environmental metagenomics analysis.},

keywords = {DNA sequences, machine learning, Perturbation Theory, Shannon Information Theory},

pubstate = {published},

tppubtype = {article}

}