García Carrasco, José Manuel

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García Carrasco, José Manuel

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García Carrasco, José Manuel

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Universidad de Murcia. Departamento de Ingeniería y Tecnología de Computadores

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Open Access
Obtaining PDC and other high-added value products from lignin by in silico genetic engineering in Novosphingobium aromaticivorans
(De Gruyter, 2026-02-25) Guil Asensio, Francisco de Asís; Fernández, Isabel María; García Carrasco, José Manuel; Ingeniería y Tecnología de Computadores; Facultades de la UMU::Facultad de Informática
Lignin, the second most abundant plant biopolymer on Earth, is produced in large quantities as waste material by many industries. Researchers have studied bacterial metabolic networks as potential candidates for integrating lignin into a biotechnological value chain. The GEM used in this work for metabolic engineering is iNovo479, which simulates the metabolism of Novosphingobium aromaticivorans DSM12444. We have conducted a study on PDC production and found several intervention strategies to help achieve this goal. These strategies include more than just blocking the ligI gene, which has been a well-known approach. Although these new strategies resulted in a lower yield of PDC relative to biomass formed, they led to a higher cell yield than deleting the ligI gene. The research presented in this paper focuses on the production of high-value compounds from lignin. Previous studies have used mutated microorganisms to produce these bioproducts from large amounts of glucose. However, biosynthesis from lignin would improve productivity and make the fermentation process more cost-effective. Through gene knockouts, we have discovered ways to ensure a minimum production of bioproducts such as acetaldehyde, citrate, glutarate, glycerol, phenol, and propanoate when growing the N. aromaticivorans strain using lignin-derived compounds as unique substrates.
Open Access
Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma
(De Gruyter, 2025-06-03) Belmonte, José María; Blanquer Blanquer, Miguel; Bernabé García, Gregorio; Jiménez Barrionuevo, Fernando; García Carrasco, José Manuel; Ingeniería y Tecnología de Computadores
This paper investigates the application of Survival Analysis (SA) techniques to forecast outcomes after autologous Hematopoietic Stem Cell Transplantation (aHSCT) for Multiple Myeloma (MM). By leveraging six SA models, we examine their predictive capabilities, measured through the Concordance Index (C-index) metric. Beyond evaluating model performance, we analyze feature importance using permutation and SHAP methods, highlighting key clinical factors such as treatment history, disease stage, and prior disease progression or relapse as critical predictors of survival. The findings suggest that while all models performed well based on the C-index, a detailed examination revealed variations in how each model processed data. Specifically, the Coxnet and Random Survival Forest models exhibited a more thorough use of clinical variables, whereas the gradient boosting models appeared to rely on a narrower range of features, potentially limiting their ability to differentiate between patients with comparable profiles. Risk predictions categorized patients into low, moderate, and high-risk levels. For lower-risk patients, the procedure showed positive outcomes, while higher-risk individuals were predicted to have limited survival benefits, recommending alternative treatments. Lastly, we propose future research to expand these models into time-to-event estimations, offering additional support for decision-making by predicting patient life expectancy post-transplant, considering their pre-transplant clinical attributes.
Embargo
Survival analysis in hematopoietic stem cell transplantation for multiple myeloma: methodology and survival predictions
(Springer Nature, 2025-04-25) Belmonte, José María; Blanquer Blanquer, Miguel; Bernabé García, Gregorio; Jiménez Barrionuevo, Fernando; García Carrasco, José Manuel; Ingeniería y Tecnología de Computadores
This work explores the application of Survival Analysis in the context of hematopoietic stem cell transplantation for multiple myeloma to enhance the predictive capacity and interpretability of transplant outcomes and inspect the patients’ overall survival. Our methodology uses all the proposed Survival Analysis models. These models are used to conduct a feature importance analysis and do some survival predictions with the interpretation of patient outcomes. The dataset, comprising 254 instances and 15 attributes, includes medical information collected from multiple myeloma patients before hematopoietic stem cell transplantation procedures. The primary objective of this work is to assess the robustness of Survival Analysis models with our data based on the concordance index metric. Through feature importance analysis, it has been revealed that variables such as the International Staging System, treatment lines, age, and disease relapse play pivotal roles in determining patient survival post-transplant. Survival predictions have been conducted for three distinct cases from the dataset, evaluating the risks patients may encounter following their treatments. These results have been validated by healthcare professionals, underscoring the reliability and applicability of this study’s findings in medical scenarios.
Open Access
A Data-Centric Algorithmic Pipeline for Enhancing Cardiac MRI Segmentation Using ViTUNeT and Quality-Aware Filtering
(MDPI, 2015-03-06) Salvador de Haro; Jesús Cámara; González Férez, María Pilar; José Manuel García; García Carrasco, José Manuel; Bernabé García, Gregorio; Ingeniería y Tecnología de Computadores; Facultades de la UMU::Facultad de Informática
The performance of deep-learning-based segmentation models is strongly dependent on the quality of the input data, which is frequently heterogeneous or degraded in real-world medical imaging scenarios. This work presents a data-centric algorithmic pipeline designed to improve cardiac MRI segmentation accuracy through systematic image enhancement and automatic slice-quality filtering. The proposed method is formalized as deterministic algorithm that combines image processing and supervised learning components. The approach integrates a contrast- and structure-preserving enhancement stage, based on bilateral filtering and adaptive histogram equalization, with a quality-aware selection algorithm. Slice quality is assessed using anatomical attributes extracted via YOLOv11sbased localization and a supervised classification model trained to identify diagnostically reliable images. When applied to transformer-based segmentation architectures such as ViTUNeT, the pipeline yields consistent improvements across all evaluation metrics without increasing model complexity or training cost. These findings emphasize the importance of algorithmic data curation as an effective strategy for enhancing robustness and stability in deep-learning segmentation pipelines and demonstrate the broader applicability of the proposed approach to computer-vision tasks involving heterogeneous or low-quality image datasets.
Open Access
Sensitivity-constrained evolutionary feature selection for imbalanced medical classification: a case study on rotator cull tear surgery prediction
(MDPI, 2025-12-08) Belmonte, José María; Jiménez Barrionuevo, Fernando; Sánchez Carpena, Gracia; Gabardo, Santiago; Martínez Catalán, Natalia; Calvo, Emilio; Bernabé García, Gregorio; García Carrasco, José Manuel; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
While most patients with degenerative rotator cuff tears respond to conservative treatment, a minority progress to surgery. To anticipate these cases under class imbalance, we propose a sensitivity-constrained evolutionary feature selection framework prioritizing surgical-class recall, benchmarked against traditional methods. Two variants are proposed: (i) a single-objective search maximizing balanced accuracy and (ii) a multi-objective search also minimizing the number of selected features. Both enforce a minimum-sensitivity constraint on the minority class to limit false negatives. The dataset includes 347 patients (66 surgical, 19%) described by 28 clinical, imaging, symptom, and functional variables. We compare against 62 widely adopted pipelines, including oversampling, undersampling, hybrid resampling, cost-sensitive classifiers, and imbalance-aware ensembles. The main metric is balanced accuracy, with surgical-class F1-score as secondary. PairwiseWilcoxon tests with a win–loss ranking assessed statistical significance. Evolutionary models rank among the top; the multi-objective variant with a Balanced Bagging Classifier performs best, achieving a mean balanced accuracy of 0.741. Selected subsets recurrently include age, tear location/severity, comorbidities, and pain/functional scores, matching clinical expectations. The constraint preserved minority-class recall without discarding or synthesizing data. Sensitivity-constrained evolutionary feature selection thus offers a data-preserving, interpretable solution for pre-surgical decision support, improving balanced performance and supporting safer triage decisions.
Open Access
A Comparative Analysis of Machine Learning and Deep Learning Approaches for Multiclass Nucleus Classifcation in Histological Images
(Wiley, 2026-01-30) Sánchez-Torres, Antonio Luis; García-Salmerón, Jesús ; González Férez, María Pilar; Bernabé García, Gregorio; García Carrasco, José Manuel; Ingeniería y Tecnología de Computadores; Kalapraveen Bagadi; Facultades de la UMU::Facultad de Informática
Precisely classifying cells in histological images is critical for early cancer diagnosis and tumor assessment. Traditional manual methods are time-consuming and labor-intensive for histopathologists, driving the development of automated approaches using machine learning (ML) and deep learning (DL). Convolutional neural networks (CNNs) and, more recently, vision transformers (ViTs) have demonstrated signifcant potential in addressing the challenges of cell classifcation by leveraging their ability to automatically extract and learn complex features from histological images. In this work, we evaluate multiple classifcation architectures applied to stained histological images to determine their efectiveness in identifying cancerous cells. We compare traditional ML models, which rely on manually extracted features such as shape and texture, against two DL-based classifers: a CNN-based model (ResNet50) and a ViT-based model. To optimize ML models, we apply principal component analysis (PCA) to refne feature selection. Meanwhile, DL models are trained on cropped cell images using two preprocessing strategies: one that includes additional surrounding cellular context and another that uses only the cell pixels. Additionally, we investigate class balancing strategies, including downsampling and oversampling through data augmentation, to mitigate the efects of dataset imbalance. Experimental results highlight the clear advantage of DL models over traditional ML approaches. ResNet50 consistently delivers robust and reliable performance across diferent preprocessing strategies, confrming its efectiveness for histopathological classifcation tasks. Meanwhile, ViTs achieve results that are comparable to those of CNNs while demonstrating a distinct advantage in classifying underrepresented nucleus classes, likely due to their ability to capture long-range dependencies. Furthermore, incorporating the surrounding cellular environment signifcantly improves classifcation accuracy, underscoring the importance of contextual information in distinguishing between diferent types of nuclei.