publications | DiegoFE

2025

Cross Sequencing Integration of Compositional Microbiome Data in Cancer

Diego Fernández-Edreira , Jose Liñares-Blanco , and Carlos Fernandez-Lozano

In Computational Intelligence Methods for Bioinformatics and Biostatistics , 2025

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RePo-SUDOE–A translational network for drug repurposing in the SUDOE space

Hugo Filipe , António Costa , André Moreira , and 8 more authors

In RExPO25 Conference , 2025

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Fecal microbiome analysis in patients with metabolic syndrome and type 2 diabetes

Laura Isabel Sinisterra Loaiza , Diego Fernández-Edreira , Jose Liñares-Blanco , and 3 more authors

PeerJ, 2025

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2024

VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria

Diego Fernández-Edreira , Jose Liñares-Blanco , Patricia V.-del-Río , and 1 more author

Computational and Structural Biotechnology Journal, 2024

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This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study’s outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.
Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Jonathan L. Golob , Tomiko T. Oskotsky , Alice S. Tang , and 49 more authors

Cell Reports Medicine, 2024

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Summary Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

2021

La Inteligencia Artificial como herramienta para la gestión y explotación de datos, informaciones y conocimientos biomédicos en entornos “Big Data” en la nube

Diego Fernández Edreira , José Liñares Blanco , Brais Castiñeiras Galdo , and 2 more authors

I+ S: Revista de la Sociedad Española de Informática y Salud, 2021

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Machine Learning analysis of the human infant gut microbiome identifies influential species in type 1 diabetes

Diego Fernández-Edreira , Jose Liñares-Blanco , and Carlos Fernandez-Lozano

Expert Systems with Applications, 2021

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Diabetes is a disease that is closely linked to genetics and epigenetics, yet mechanisms for clarifying the onset and/or progression of the disease have sometimes not been fully managed. In recent years and due to the large number of recent studies, it is known that changes in the balance of the microbiota can cause a high battery of diseases, including diabetes. Machine Learning (ML) techniques are able to identify complex, non-linear patterns of expression and relationships within the data set to extract intrinsic knowledge without any biological assumptions about the data. At the same time, mass sequencing techniques allow us to obtain the metagenomic profile of an individual, whether it is a body part, organ or tissue, and thus identify the composition of a given microbe. The great increase in the development of both technologies in their respective fields of study leads to the logical union of both to try to identify the bases of a complex disease such as diabetes. To this end, a Random Forest model has been developed at different taxonomic levels, obtaining results above 0.80 in AUC for families and above 0.98 at species level, following a strict experimental design to ensure that results are compared under equal conditions. It is identified how, in infants, the species Bacteroides uniformis, Bacteroides dorei and Bacteroides thetaiotaomicron are reduced in the microbiota of those with T1D, while, the populations of Prevotella copri increase slightly and that of Bacteroides vulgatus is much higher. Finally, thanks to the more specific metagenomic signature at species level, a model has been generated to predict those seroconverted patients not previously diagnosed with diabetes but who have expressed at least two of the autoantibodies analysed.

2020

Identification of Prevotella, Anaerotruncus and Eubacterium Genera by Machine Learning Analysis of Metagenomic Profiles for Stratification of Patients Affected by Type I Diabetes

Diego Fernández-Edreira , Jose Liñares-Blanco , and Carlos Fernandez-Lozano

Proceedings, 2020

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Previous works have reported different bacterial strains and genera as the cause of different clinical pathological conditions. In our approach, using the fecal metagenomic profiles of newborns, a machine learning-based model was generated capable of discerning between patients affected by type I diabetes and controls. Furthermore, a random forest algorithm achieved a 0.915 in AUROC. The automation of processes and support to clinical decision making under metagenomic variables of interest may result in lower experimental costs in the diagnosis of complex diseases of high prevalence worldwide.

2018

Isolation and characterization of clones with hydrolase activity from a metagenomic library of thermal water.

Diego Fernández-Edreira

2018

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Thermostable enzymes are of enormous importance due to their characteristics, which confer them greater stability and activity under extreme conditions. Moreover, enzymes with lipolytic activity have high potential and applicability in the biotechnological industry, being used in a wide range of sectors, such as the food-processing industry, the paper industry, the detergent industry, and so on. Hence, there is interest in the bioprospecting of these enzymes in metagenomics libraries from thermal waters, as their high capacity for action under these conditions facilitates their later use in industry.