Examinando por Autor "Lozano-Paredes, Dafne"

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Discovering Genetic Variants in Hypertrophic Cardiomyopathy with Multiple Machine Learning Techniques
(Institute of Electrical and Electronics Engineers, 2025-05-26) Lozano-Paredes, Dafne; Bote-Curiel, Luis; Sabater-Molina, María; Bielza, Concha; Gimeno-Blanes, Juan R.; Muñoz-Romero, Sergio
Hypertrophic cardiomyopathy is known to have strong genetic foundations. However, only some studies have addressed the complex network of co-expressed genes and variants that modify the phenotype. Machine learning methods offer robust information discovery when dealing with high-dimensional datasets. We aimed to perform relevance and interaction analysis on genetic variants from hypertrophic cardiomyopathy patients using diverse machine learning techniques, with the following stages: (a) Statistical univariate techniques (with various p-value adjustment methods) identified relevant variants; (b) Linear classifiers (support vector machines, Fisher discriminant analysis) provided combined relevance based on feature weights; (c) Informative variable identifier method and Bayesian networks explained inter-variant relationships; (d) Manifold learning of low-dimensional latent spaces gave interpretable representations of groups; (e) Linkage disequilibrium matrices and frequency tables discovered associations between variants. We analyzed 61 patients and 67 controls with genetic information comprising 216 variants from a genetic panel of 15 genes. Across all methodologies, ten variants were consistently identified as significant, with 22 total variants significant in at least three out of five methods. Machine learning has been found to detect disease-associated variants, including pathogenic founder variants (11:47357494, 11:47360070, 11:47372137). This methodology allows for identifying potential disease modulators while accounting for relevance and interactions among variants.
Interpreting frequency evolution in ventricular fibrillation using embeddings and deep learning methods
(Elsevier, 2025-08-15) Lozano-Paredes, Dafne; Sánchez-Muñoz , Juan José; Bote-Curiel, Luis; Melgarejo-Meseguer, Francisco M.; Gil-Izquierdo, Antonio; Gimeno-Blanes, F. Javier; Rojo-Álvarez, José Luis
Recently, the necessity for advanced tools to scrutinize ventricular fibrillation (VF) has been highlighted. Despite progress in the field, applying deep learning techniques and manifold interpretations in clinical settings remains underexplored. This study aims to evaluate the effectiveness of low-dimensional embeddings for distinguishing VF. We analyzed VF from three clinical conditions: patients during cardiopulmonary bypass, dogs administered with different drugs, and implantable cardioverter defibrillator devices with varying offset characteristics. We employed several algorithms, including uniform manifold approximation and projection embeddings, temporal convolutional networks, fully connected networks, and Kolmogorov–Arnold networks. Our experiments revealed that VF dynamics can be categorized based on frequency evolution, and the result can be interpreted based on clinical knowledge. However, each dataset has unique characteristics, leading to variations in the best-performing method. These differences may arise because some VF types are more easily identifiable. Our findings prove that longer signals differentiate VF types more clearly as the frequency evolution becomes clearer over extended periods. Across the same dataset, methods showed only slight differences in performance. Notably, for one dataset, two different drugs in dogs showed similar frequency patterns. For the rest of the datasets and methods, accuracy ranged between 0.68 and 0.86, precision ranged from 0.69 to 0.84, recall ranged from 0.68 to 0.84, and F1 scores ranged from 0.68 to 0.84. We conclude that low-dimensional embeddings are an effective method for characterizing VF types, and these methods can support ongoing research that aims to clarify the mechanisms of VF.
Online automatic detection of phrenic nerve activation during cryoablation procedure for atrial fibrillation treatment
(Elsevier, 2025-03) Gil-Izquierdo, Antonio; Mateos-Gaitán, Roberto; Melgarejo-Meseguer, Francisco M.; Gimeno-Blanes, F. Javier; Lozano-Paredes, Dafne; Sánchez-Muñoz, Juan José; García-Alberola, Arcadi; Rojo-Álvarez, José Luis
Abstract Background and Aim: Cryoballoon ablation is an effective technique for treating Atrial Fibrillation (AF). Its application in the pulmonary vein antrum poses a potential risk of phrenic nerve damage due to its anatomic proximity. Manual protocols are implemented during the ablation procedure to mitigate this risk, although these may be susceptible to subjectivity and variations. In this work, we propose an online system capable of automatically detecting the phrenic nerve integrity during the cryoablation procedure for AF in the pulmonary veins. The system performs digital processing of the ECG signals recorded during the ablation process, detects and segments the ECG signals, and uses a machine learning classifier to infer the risk of damage. Methods: The used dataset consisted of monitoring system signals obtained from the cryoablation procedures of ten AF patients from Virgen de la Arrixaca University Clinical Hospital in Murcia, Spain. The first stage involves signal processing of the ECG leads, using noise filtering and delineation to unmask any residual cellular potential during phrenic nerve stimulation. A comparative analysis was conducted where the electrocatheter was placed near the phrenic nerve to stimulate it and when the electrocatheter was intentionally displaced, resulting in the phrenic nerve not being stimulated despite an electrical pulse being applied. The detection stage used a linear support vector classifier for both scenarios. Results: It was possible to automatically classify the level of muscle activity from the phrenic nerve with high accuracy in this known-solution dataset. An online system was created capable of performing and synchronizing all the described stages to manage the signal extracted from the monitoring system. Conclusion: The system presented here can be a valuable tool for clinical practice, enabling the identification of specific pacing pulses when phrenic nerve involvement occurs, eventually and probably minimizing the use of manual protocols subject to interpretation biases.
Pymportx: Facilitating next-generation transcriptomics analysis in Python
(Oxford University Press, 2024-11-15) Pena González, Paula; Lozano-Paredes, Dafne; Rojo-Álvarez, José Luis; Bote-Curiel, Luis; Sánchez-Arévalo Lobo, Víctor
The efficient importation of quantified gene expression data is pivotal in transcriptomics. Historically, the R package Tximport addressed this need by enabling seamless data integration from various quantification tools. However, the Python community lacked a corresponding tool, restricting cross-platform bioinformatics interoperability. We introduce Pymportx, a Python adaptation of Tximport, which replicates and extends the original package’s functionalities. Pymportx maintains the integrity and accuracy of gene expression data while improving processing speed and integration within the Python ecosystem. It supports new data formats and includes tools for enhanced data exploration and analysis. Available under the MIT license, Pymportx integrates smoothly with Python’s bioinformatics tools, facilitating a unified and efficient workflow across the R and Python ecosystems. This advancement not only broadens access to Python’s extensive toolset but also fosters interdisciplinary collaboration and the development of cutting-edge bioinformatics analyses.