Examinando por Autor "Bote-Curiel, Luis"

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A Resampling Univariate Analysis Approach to Ovarian Cancer from Clinical and Genetic Data
(IEEE, 2021-02-08) Bote-Curiel, Luis; Ruiz-Llorente, Sergio; Muñoz-Romero, Sergio; Yagüe-Fernández, Mónica; Barquín, Arantzazu; García-Donas, Jesús; Rojo-Álvarez, José Luis
Ovarian cancer (OC) is the second most common gynecological malignancy and the gynecological tumor with the worst prognosis. To try to improve this situation, Data Science technologies could be a useful tool to help clinicians to know more about the disease. In our case, we are interested in exploring OC data to discover relationships between clinical and genetic factors and the disease progression. For it, we propose an analysis framework for simple and univariate statistical descriptions of features of different types, based on bootstrap resampling. Foremost, we define the framework for metric, categorical, and dates variables and determine what are the advantages and disadvantages of using different bootstrap resampling strategies, based on their statistical basis. Then, we use it to perform a univariate analysis over an OC dataset that allows to explore how is the disease progression, having platinum-free interval as indicator, in relation to clinical and genetic features of different types. Also, it provides a first set of variables possibly relevant for survival prediction. Results obtained show that some features have led to individual differences between both platinum resistant (<; 6 months) and platinum sensitive(>6 months) groups. It can be concluded that this could be an indicator that the database could be discriminatory for the hypotheses studied, though it is convenient to make multivariate analyses to check how relationships among features are influenced.
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.
Medical Needs Related to the Endoscopic Technology and Colonoscopy for Colorectal Cancer Diagnosis
(BMC, 2021) Ortega-Morán, Juan Francisco; Azpeitia, Águeda; Sánchez-Peralta, Luisa F.; Bote-Curiel, Luis; Pagador, Blas; Cabezón, Virginia; Lopez Saratxaga, Cristina; Sánchez-Margallo, Francisco M.
Background The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies. Methods Semi-structured interviews and an online survey were used. Results Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation. Conclusions This study shows some undertaken initiatives to meet the detected medical needs and challenges to be solved. The great potential of advanced optical technologies suggests their use for a better polyp detection and classification since they provide additional functional and structural information than the currently used image enhancement technologies. The inspection of remaining tissue of diminutive polyps (< 5 mm) should be addressed to reduce recurrence rates. Few progresses have been made in estimating the infiltration depth. Detection and classification methods should be combined into one CAD system, providing visual aids over polyps for detection and displaying a Kudo-based diagnosis suggestion to assist the endoscopist on real-time decision making. Estimated size and location of polyps should also be provided. Endoscopes with 360° vision are still a challenge not met by the mechanical and optical systems developed to improve the colon inspection. Patients and healthcare providers should be trained to improve the patient’s bowel preparation.
Multivariate feature selection and autoencoder embeddings of ovarian cancer clinical and genetic data
(Elsevier, 2022) Bote-Curiel, Luis; Ruiz-Llorente, Sergio; Muñoz-Romero, Sergio; Yagüe-Fernández, Mónica; Barquín, Arantzazu; García-Donas, Jesús; Rojo-Álvarez, José Luis
Although certain genetic alterations have been defined as predictive and prognostic biomarkers in the context of ovarian cancer (OC), data science methods represent alternative approaches to identify novel correlations and define relevant markers in these gynecological tumors. Considering this potential, our work focused both on clinical and genomic data information collected from patients with OC to identify relationships between clinical and genetic factors and disease progression-related variables. For this aim, we proposed two analyses: (1) a nonlinear exploration of an OC dataset using autoencoders, a type of neural network that can be used as a feature extraction tool to represent a dataset in 3-dimensional latent space, so that we could assess whether there are intrinsic or natural nonlinear separability patterns between disease progression groups (in our case, platinum-sensitive and platinum-resistant groups); and (2) the identification of relevant variable relationships by means of an adaptation of the informative variable identifier (IVI), a feature selection method that labels each input feature as informative or noisy with respect to the task at hand, identifies the relationships among features, and builds a ranking of features, allowing us to study which input features and relationships may be most informative for the OC disease progression classification to define new biomarkers involved in disease progression. Our interest has been in clinical and genetic factors and in the combination of clinical features and genetic profile. Results with autoencoders suggest a pattern of separability between disease progression groups in the clinical part and for the combination of genes and clinical features of the OC dataset, that is increased via supervised fine tuning. In the genetic part, this pattern of separability is not observed, but it is more defined when a supervised fine tuning is performed. Results of the IVI-mediated feature selection method show significance for relevant clinical variables (such as type of surgery and neoadjuvant chemotherapy), some mutation genes, and low-risk genetic features. These results highlight the efficacy of the considered approaches to better understand the clinical course of OC.
Multivariate Feature Selection and Autoencoder Embeddings of Ovarian Cancer Clinical and Genetic Data
(Elsevier Ltd, 2022) Bote-Curiel, Luis; Ruiz-Llorente, Sergio; Muñoz-Romero, Sergio; Yagüe-Fernández, Mónica; Barquín, Arantzazu; García-Donas, Jesús; Rojo-Álvarez, José Luis
Although certain genetic alterations have been defined as predictive and prognostic biomarkers in the context of ovarian cancer (OC), data science methods represent alternative approaches to identify novel correlations and define relevant markers in these gynecological tumors. Considering this potential, our work focused both on clinical and genomic data information collected from patients with OC to identify relationships between clinical and genetic factors and disease progression-related variables. For this aim, we proposed two analyses: (1) a nonlinear exploration of an OC dataset using autoencoders, a type of neural network that can be used as a feature extraction tool to represent a dataset in 3-dimensional latent space, so that we could assess whether there are intrinsic or natural nonlinear separability patterns between disease progression groups (in our case, platinum-sensitive and platinum-resistant groups); and (2) the identification of relevant variable relationships by means of an adaptation of the informative variable identifier (IVI), a feature selection method that labels each input feature as informative or noisy with respect to the task at hand, identifies the relationships among features, and builds a ranking of features, allowing us to study which input features and relationships may be most informative for the OC disease progression classification to define new biomarkers involved in disease progression. Our interest has been in clinical and genetic factors and in the combination of clinical features and genetic profile. Results with autoencoders suggest a pattern of separability between disease progression groups in the clinical part and for the combination of genes and clinical features of the OC dataset, that is increased via supervised fine tuning. In the genetic part, this pattern of separability is not observed, but it is more defined when a supervised fine tuning is performed. Results of the IVI-mediated feature selection method show significance for relevant clinical variables (such as type of surgery and neoadjuvant chemotherapy), some mutation genes, and low-risk genetic features. These results highlight the efficacy of the considered approaches to better understand the clinical course of OC.
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.
Text Analytics and Mixed Feature Extraction in Ovarian Cancer Clinical and Genetic Data
(IEEE, 2021) Bote-Curiel, Luis; Ruiz-Llorente, Sergio; Muñoz-Romero, Sergio; Yagüe-Fernández, Mónica; Barquín, Arantzazu; García-Donas, Jesús; Rojo-Álvarez, José Luis
Developments of richer integrative analysis methods for oncological studies are needed for efficiently leveraging the amount of clinical and genetic data available to provide the clinicians with better information. However, analyses of this nature often require mixing data of different types, which are not immediate to address jointly with classical methods. In this work, our aim is to find relationships between clinical and genetic features of different types (metric, categorical, and text) and the ovarian cancer (OC) disease progression. To this end, we first propose a univariate statistical method for text type applying bootstrap resampling to Bag of Words and Latent Dirichlet Allocation in order to include as features the free-text fields of the health recordings. Secondly, we extend bootstrap resampling for metric and categorical feature extraction with Principal Component Analysis (PCA) and Multiple Correspondence Analysis (MCA), respectively. We subsequently formulate a novel and integrative method for jointly considering metric, categorical, and text features. Results obtained in text analysis indicate individual differences in some words between two OC patients groups categorised according to their sensitivity to platinum drugs. These results indicate separability between both groups for text features. Also, regarding the multivariate analysis, clinical data results showed separability patterns for the three methods analysed according to the platinum-sensitivity degree. The use of these analytical tools in our OC cohort has allowed us to demonstrate their strengths by confirming the predictive and prognostic role of widely-known clinical and genetic variables (BRCA status, value of adjuvant therapy and optimal resection, or family history) and demonstrating significant associations in other variables whose role in OC development has been studied to a lesser extent (such as PMS1, GPC3, and SLX4 genes). These results highlight the value of implementing these approaches for the identification of novel biomarkers in the context of OC.
Towards Explainable Artificial Intelligence in Machine Learning: A study on efficient Perturbation-Based Explanations
(Elsevier, 2025-09-01) Gómez-Talal, Ismael; Azizsoltani, Mana; Bote-Curiel, Luis; Rojo-Álvarez, José Luis; Singh, Ashok
Explainable Artificial Intelligence (XAI) is critical for validating and trusting the decisions made by Machine Learning (ML) models, especially in high-stakes domains such as healthcare and finance. However, existing XAI methods often face significant computational challenges. To address this gap, this paper introduces a novel Perturbation-Based Explanation (PeBEx) method and comprehensively evaluates it versus local interpretable model-agnostic explanation approach (LIME) and SHapley Additive exPlanations (SHAP) across multiple datasets and ML models to assess explanation quality and computational efficiency. PeBEx leverages perturbation-based strategies to systematically alter input features and observe changes in model predictions to determine feature importance. This method not only offers superior computational efficiency, leading to scalability and efficiency for complex models on large datasets. Through testing on both synthetic and public datasets using eight ML models, we uncover the relative strengths and limitations of each XAI method in terms of explanation accuracy, fidelity, and computational demands. Our results show that while SHAP and LIME provide detailed explanations, they often suffer from high computational costs, particularly with complex models like Multi-Layer Perceptron (MLP). Conversely, PeBEx demonstrates superior efficiency and scalability, making it particularly suitable for applications that require rapid response times without compromising explanation quality. We conclude by proposing potential enhancements for PeBEx, including its adoption in a wider array of large-scale models. This study not only advances our understanding of the computational aspects of XAI but also proposes PeBEx as a viable solution for improving the efficiency, scalability, and applicability of explainability in ML.
Understanding the disparities in Mathematics performance: An interpretability-based examination
(Elsevier, 2024-07) Gómez-Talal, Ismael; Bote-Curiel, Luis; Rojo-Álvarez, José Luis
Problem: Educational disparities in Mathematics performance are a persistent challenge. This study aims to unravel the complex factors contributing to these disparities among students internationally, with a focus on the interpretability of the contributing factors. Methodology: Utilizing data from the Programme for International Student Assessment (PISA), we conducted rigorous preprocessing and variable selection to prepare for applying binary classification interpretability models. These models were trained using the Stratified K-Fold technique to ensure balanced representation and assessed using six key metrics. Solution: By applying interpretability models such as Shapley Additive Explanations (SHAP) analysis, we identified critical factors impacting student performance, including reading accessibility, critical thinking skills, gender, and geographical location. Results: Our findings reveal significant disparities linked to resource availability, with students from lower socioeconomic backgrounds possessing fewer books and demonstrating lower performance in Mathematics. The geographical analysis highlighted regional educational disparities, with certain areas consistently underperforming in PISA assessments. Gender also emerged as a determinant, with females contributing differently to performance levels across the spectrum. Conclusion: The study provides insights into the multifaceted determinants of student Mathematics performance and suggests potential avenues for future research to explore global interpretability models and further investigate the socioeconomic, cultural, and educational factors at play