BioMedInformatics

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized primarily by motor impairments due to the loss of dopaminergic neurons. Despite extensive research, the precise causes of PD remain unknown, and reliable non-invasive biomarkers are still lacking. This study aimed to explore gene expression profiles in peripheral blood to identify potential biomarkers for PD using machine learning approaches. We analyzed microarray-based gene expression data from 105 individuals (50 PD patients, 33 with other neurodegenerative diseases, and 22 healthy controls) obtained from the GEO database (GSE6613). Preprocessing was performed using the “affy” package in R with Expresso normalization. Feature selection and classification were conducted using a decision tree approach (C4.5/J48 algorithm in WEKA), and model performance was evaluated with 10-fold cross-validation. Additional classifiers such as Support Vector Machine (SVM), the Naive Bayes classifier and Multilayer Perceptron Neural Network (MLP) were used for comparison. ROC curve analysis and Gene Ontology (GO) enrichment analysis were applied to the selected genes. A nine-gene decision tree model (TMEM104, TRIM33, GJB3, SPON2, SNAP25, TRAK2, SHPK, PIEZO1, RPL37) achieved 86.71% accuracy, 88% sensitivity, and 87% specificity. The model significantly outperformed other classifiers (SVM, Naive Bayes, MLP) in terms of overall predictive accuracy. ROC analysis showed moderate discrimination for some genes (e.g., TRAK2, TRIM33, PIEZO1), and GO enrichment revealed associations with synaptic processes, inflammation, mitochondrial transport, and stress response pathways. Our decision tree model based on blood gene expression profiles effectively discriminates between PD, other neurodegenerative conditions, and healthy controls, offering a non-invasive method for potential early diagnosis. Notably, TMEM104, TRIM33, and SNAP25 emerged as promising candidate biomarkers, warranting further investigation in larger and synthetic datasets to validate their clinical relevance. Full article

Background: Reconstructing gene regulatory networks (GRNs) from gene expression data remains a major challenge in systems biology due to the inherent complexity of biological systems and the limitations of existing reconstruction methods, which often yield high false-positive rates. This study aims to develop a robust and adaptive approach to enhance the accuracy of inferred GRNs by integrating multiple modelling paradigms. Methods: We introduce EvoFuzzy, a novel algorithm that integrates evolutionary computation and fuzzy logic to aggregate GRNs reconstructed using Boolean, regression, and fuzzy modelling techniques. The algorithm initializes an equal number of individuals from each modelling method to form a diverse population, which evolves through fuzzy trigonometric differential evolution. Gene expression values are predicted using a fuzzy logic-based predictor with confidence levels, and a fitness function is applied to identify the optimal consensus network. Results: The proposed method was evaluated using simulated benchmark datasets and a real-world SOS gene repair dataset. Experimental results demonstrated that EvoFuzzy consistently outperformed existing state-of-the-art GRN reconstruction methods in terms of accuracy and robustness. Conclusions: The fuzzy trigonometric differential evolution approach plays a pivotal role in refining and aggregating multiple network outputs into a single, optimal consensus network, making EvoFuzzy a powerful and reliable framework for reconstructing biologically meaningful gene regulatory networks. Full article

Extensive research is being carried out on the application of $\alpha$ particles for cancer treatment. A key challenge in $\alpha$ therapy is how to deliver the $\alpha$ emitters to the tumour. In AlphaGlue, a novel treatment delivery concept, the $\alpha$ emitters are suspended in a thin layer of glue that is put on top of the tumour. In principle, this should be an easy and safe way to apply $\alpha$ therapy. In this study, the effectiveness of AlphaGlue is evaluated using GEANT4 and GEANT4-DNA simulations to calculate the DNA damage as a function of depth. Two radionuclides are considered in this work, ²¹¹At and ²²⁴Ra. The results indicate that, as a concept, the method offers a promising hypothesis for treating superficial tumours, such as skin cancer, when ²²⁴Ra is applied directly on the tissue and stabilized with a glue layer. This results in 2$\times {10}^{-5}$ complex double strand breaks and 5$\times {10}^{-5}$ double strand breaks at 5 mm depth per applied ²²⁴Ra atom. When applying a ²²⁴Ra atom concentration of $(4.35\pm 0.2)\times {10}^{11}/$cm² corresponding to an activity of $(21.8\pm 1)\mathsf{\mu }$Ci/cm² on the skin surface, the RBE weighted dose exceeds 20 Gy at 5 mm depth. Hence, there is significant cell death at 5 mm into the tissue; a depth matching clinical requirements for skin cancer treatment. Given the rapidly falling weighted dose versus depth curve, the treatment depth can be tuned with good precision. The results of this study show that AlphaGlue is a promosing treatment and open the pathway towards the next stage of the research, which includes in-vitro studies. Full article

Background: Urinary tract infections (UTIs) remain among the most common bacterial infections, yet reliable risk stratification remains challenging. Serum vitamin D has been linked to immune regulation, but its predictive role in UTI subtypes is unclear. Methods: We analyzed 332 de-identified clinical records using six machine learning algorithms: Extra Trees, Gradient Boosting, XGBoost, Logistic Regression, Random Forest, and LightGBM. Two preprocessing strategies were applied: (i) removing rows with missing fasting blood sugar (FBs) and HbA1c, and (ii) dropping columns with Null FBs and HbA1c values. Model performance was evaluated using 10-fold cross-validation. Results: Serum vitamin D showed weak correlations with UTI subtypes but modest importance in tree-based models. The highest predictive accuracy was obtained with Extra Trees (0.9510) under the row-removal strategy and Random Forest (0.9525) under the column-dropping strategy. Models excluding vitamin D maintained comparable accuracy, suggesting minimal impact on overall predictive performance. Conclusions: Machine learning models demonstrated high accuracy and robustness in predicting UTI subtypes across preprocessing strategies. While vitamin D contributes as a supportive feature, it is not essential for reliable prediction. These findings highlight the adaptability and clinical utility of both vitamin D-inclusive and vitamin D-exclusive models, supporting deployment in diverse healthcare settings. Full article

Accurate spleen segmentation in abdominal CT scans remains a critical challenge in medical image analysis due to variable morphology, low tissue contrast, and proximity to similar anatomical structures. This paper presents an enhanced U-Net architecture that addresses these challenges through multi-slice contextual integration and interpretable deep learning. Our approach incorporates three-channel inputs from adjacent CT slices, implements a hybrid loss function combining Dice and binary cross-entropy terms, and integrates Grad-CAM visualization for enhanced model interpretability. Comprehensive evaluation on the Medical Decathlon dataset demonstrates superior performance, with a Dice similarity coefficient of 0.923 ± 0.04, outperforming standard 2D approaches by 3.2%. The model exhibits robust performance across varying slice thicknesses, contrast phases, and pathological conditions. Grad-CAM analysis reveals focused attention on spleen–tissue interfaces and internal vascular structures, providing clinical insight into model decision-making. The system demonstrates practical applicability for automated splenic volumetry, trauma assessment, and surgical planning, with processing times suitable for clinical workflow integration. Full article

Background: The selection of machine learning (ML) models in the biomedical sciences often relies on global performance metrics. When these metrics are closely clustered among candidate models, identifying the most suitable model for real-world deployment becomes challenging. Methods: We developed a novel composite framework that integrates visual inspection of Model Scoring Distribution Analysis (MSDA) with a new scoring metric (MSDscore). The methodology was implemented within the Digital Phenomics platform as the MSDanalyser tool and tested by generating and evaluating 27 predictive models developed for breast, lung, and renal cancer prognosis. Results: Our approach enabled a detailed inspection of true-positive, false-positive, true-negative, and false-negative distributions across the scoring space, capturing local performance patterns overlooked by conventional metrics. In contrast with the minimal variation between models obtained by global metrics, the MSDA methodology revealed substantial differences in score region behaviour, allowing better discrimination between models. Conclusions: Integrating our composite framework alongside traditional performance metrics provides a complementary and more nuanced approach to model selection in clinical and biomedical settings. Full article

This paper investigates the state of substance use disorder (SUD) and the frequency of substance use by utilizing three unsupervised machine learning techniques, based on the Diagnostic and Statistical Manual 5 (DSM-5) of mental health disorders. We used data obtained from the National Survey on Drug Use and Health (NSDUH) 2019 database with random participants who had undergone clinical assessments by mental health professionals and whose clinical diagnoses were not known. This approach classifies SUD status by discovering patterns or correlations from the trained model. Our results were analyzed and validated by a mental health professional. The three unsupervised machine learning techniques that we used comprised k-means clustering, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN), which were applied to alcohol, marijuana, and cocaine datasets. The clustering results were validated using the silhouette score and the 95% confidence interval (CI). The results from this study may be used to supplement psychiatric evaluations. Full article

Background: To propose an automatic liver and hepatic vessel segmentation solution based on a stacking model and decision fusion. This model combines the decisions of multiple models to achieve increased accuracy. It exhibits improved robustness due to the reduction of individual errors. Flexibility is also a key asset, with combination methods such as majority voting or weighted averaging. The model enables managing the uncertainty associated with individual decisions to obtain a more reliable final decision. The combination of decisions improves the overall accuracy of the system. Methods: This research introduces a new deep learning-based architecture for automatically segmenting hepatic vessels and tumors from CT scans, utilizing stacking, decision fusion, and deep transfer learning to achieve high-accuracy and rapid segmentation. This study employed two distinct datasets: the external “Medical Segmentation Decathlon (MSD) task 08” dataset and an internal dataset procured from Ibn Sina University Hospital encompassing a cohort of 112 patients with chronic liver disease who underwent contrast-enhanced abdominal CT scans. Results: The proposed segmentation model reached a DSC of 83.21 and an IoU of 72.76 for hepatic vasculature and tumor segmentation, thereby exceeding the performance benchmarks established by the majority of antecedent studies. Conclusions: This study introduces an automated method for liver vessels and liver tumor segmentation, combining precision and stability to bridge the clinical gap. Furthermore, decision fusion-based stacking models have a significant impact on clinical applications by enhancing diagnostic accuracy, enabling personalized care through the integration of genetic, environmental, and clinical data, optimizing clinical trials, and facilitating the development of personalized medicines and therapies. Full article

Background: Pediatric Intensive Care Unit (PICU) outcome prediction is challenging, and machine learning (ML) can enhance it by leveraging large datasets. Methods: We built an ML model to predict PICU outcomes (“Death vs. Survival”, “Death or Morbidity vs. Survival without morbidity”, and “New Morbidity vs. Survival without new morbidity”) using the Trichotomous Outcome Prediction in Critical Care (TOPICC) study dataset. The model used the Light Gradient-Boosting Machine (LightGBM) algorithm, which was trained on 85% of the dataset and tested on 15% utilizing 10-fold cross validation. Results: The model demonstrated high accuracy across all dichotomies, with 0.98 for “Death vs. Survival”, 0.92 for “Death or New Morbidity vs. Survival without New Morbidity”, and 0.93 for “New Morbidity vs. Survival without New Morbidity.” The AUC-ROC values were also strong, at 0.89, 0.79, and 0.74, respectively. The precision was highest for “Death vs. Survival” (0.92), followed by 0.45 and 0.30 for the other dichotomies. The recalls were low, at 0.26, 0.31, and 0.34, reflecting the model’s difficulty in identifying all positive cases. The AUC-PR values (0.43, 0.37, and 0.20) highlight this trade-off. Conclusions: The LightGBM model demonstrated a predictive performance comparable to previously reported logistic regression models in predicting PICU outcomes. Future work should focus on enhancing the model’s performance and further validation across larger datasets to assess the model’s generalizability and real-world applicability. Full article

Background: Quantum machine learning (QML) holds significant promise for advancing medical image classification. However, its practical application to large-scale, high-resolution datasets is constrained by the limited number of qubits and the inherent noise in current quantum hardware. Methods: In this study, we propose the Fused Quantum Dual-Backbone Network (FQDN), a novel hybrid architecture that integrates classical convolutional neural networks (CNNs) with quantum circuits. This design is optimized for the noisy intermediate-scale quantum (NISQ), enabling efficient computation despite hardware limitations. We evaluate FQDN on the task of gastrointestinal (GI) disease classification using wireless capsule endoscopy (WCE) images. Results: The proposed model achieves a substantial reduction in parameter complexity, with a 29.04% decrease in total parameters and a 94.44% reduction in trainable parameters, while outperforming its classical counterpart. FQDN achieves an accuracy of 95.80% on the validation set and 95.42% on the test set. Conclusions: These results demonstrate the potential of QML to enhance diagnostic accuracy in medical imaging. Full article

Background: Virtual coaching can help people adopt new healthful behaviors by encouraging them to set specific goals and helping them review their progress. One challenge in creating such systems is analyzing clients’ statements about their activities. Limiting people to selecting among predefined answers detracts from the naturalness of conversations and user engagement. Large Language Models (LLMs) offer the promise of covering a wide range of expressions. However, using an LLM for simple entity extraction would not necessarily perform better than functions coded in a programming language, while creating higher long-term costs. Methods: This study uses a real data set of annotated human coaching dialogs to develop LLM-based models for two training scenarios: one that generates pattern-matching functions and the other which does direct extraction. We use models of different sizes and complexity, including Meta-Llama, Gemma, and ChatGPT, and calculate their speed and accuracy. Results: LLM-generated pattern-matching functions took an average of 10 milliseconds (ms) per item as compared to 900 ms. (ChatGPT 3.5 Turbo) to 5 s (Llama 2 70B). The accuracy for pattern matching was 99% on real data, while LLM accuracy ranged from 90% (Llama 2 70B) to 100% (ChatGPT 3.5 Turbo), on both real and synthetically generated examples created for fine-tuning. Conclusions: These findings suggest promising directions for future research that combines both methods (reserving the LLM for cases that cannot be matched directly) or that use LLMs to generate synthetic training data with more expressive variety which can be used to improve the coverage of either generated codes or fine-tuned models. Full article

Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in underserved areas. This study aimed to develop and describe a co-design process for creating a Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-compliant, AI-powered Smart Assistant (SmartApp) to monitor neurocognitive decline, while ensuring accessibility, clinical relevance, and responsible AI integration. Methods: A co-design framework was applied using a novel combination of Agile principles and the Double Diamond Model (DDM). More than twenty iterative Scrum sprints were conducted, involving key stakeholders such as clinicians (psychiatrist, psychologist, physician), designers, students, and academic researchers. Prototype testing and design workshops were organised to gather structured feedback. Feedback was systematically incorporated into subsequent iterations to refine functionality, usability, and clinical applicability. Results: The iterative process resulted in a SmartApp that integrates a DSM-5-based screening tool with 24 items across key cognitive domains. Key features include longitudinal tracking of cognitive performance, comparative visual graphs, predictive analytics using a regression-based machine learning module, and adaptive user interfaces. Workshop participants reported high satisfaction with features such as simplified navigation, notification reminders, and clinician-focused reporting modules. Conclusions: The findings suggest that combining co-design methods with Agile/DDM frameworks provides an effective pathway for developing AI-powered clinical tools as per responsible AI standards. The SmartApp offers a clinically relevant, user-friendly platform for dementia screening and monitoring, with potential to support vulnerable populations through scalable, responsible digital health solutions. Full article

Virtual-reality exposure therapy (VRET) is an emerging treatment for psychiatric disorders that enables immersive and controlled exposure to anxiety-provoking stimuli. Recent developments integrate real-time physiological monitoring, including heart rate (HR), electrodermal activity (EDA), and electroencephalography (EEG), to dynamically tailor therapeutic interventions. This systematic review examines studies that combine VRET with physiological data to adapt virtual environments in real time. A comprehensive search of major databases identified fifteen studies meeting the inclusion criteria: all employed physiological monitoring and adaptive features, with ten using biofeedback to modulate exposure based on single or multimodal physiological measures. The remaining studies leveraged physiological signals to inform scenario selection or threat modulation using dynamic categorization algorithms and machine learning. Although findings currently show an overrepresentation of anxiety disorders, recent studies are increasingly involving more diverse clinical populations. Results suggest that adaptive VRET is technically feasible and offers promising personalization benefits; however, the limited number of studies, methodological variability, and small sample sizes constrain broader conclusions. Future research should prioritize rigorous experimental designs, standardized outcome measures, and greater diversity in clinical populations. Adaptive VRET represents a frontier in precision psychiatry, where real-time biosensing and immersive technologies converge to enhance individualized mental health care. Full article

Background: Human Metapneumovirus (HMPV) is a respiratory virus in the Pneumoviridae family. HMPV is an enveloped, negative-sense RNA virus encoding three surface proteins: SH, G, and F. The highly immunogenic fusion (F) protein is essential for viral entry and a key target for vaccine development. The F protein exists in two conformations: prefusion and postfusion. The prefusion form is highly immunogenic and considered a potent vaccine antigen. However, this conformation needs to be stabilized to improve its immunogenicity for effective vaccine development. Specific mutations are necessary to maintain the prefusion state and prevent it from changing to the postfusion form. Methods: In silico mutagenesis was performed on the C-terminal domain of the pre-F protein, focusing on five amino acids at positions 469 to 473 (LVDQS), using the established pre-F structure (PDB: 8W3Q) as the reference. The amino acid sequence was sequentially mutated based on hydrophobicity, resulting in mutants M1 (IIFLL), M2 (LLIVL), M3 (WWVLL), and M4 (YMWLL). Increasing hydrophobicity was found to enhance protein stability and structural rigidity. Results: Epitope mapping revealed that all mutants displayed significant B and T cell epitopes similar to the reference protein. The structure and stability of all mutants were analyzed using molecular dynamics simulations, free energy calculations, and secondary structure analysis. Based on the lowest RMSD, clash score, MolProbity value, stable radius of gyration, and low RMSF, the M1 mutant demonstrated superior structural stability. Conclusions: Our findings indicate that the M1 mutant of the pre-F protein could be the most stable and structurally accurate candidate for vaccine development against HMPV. Full article

Breast cancer remains a critical global health challenge, with over 2.1 million new cases annually. This review systematically evaluates recent advancements (2022–2024) in machine and deep learning approaches for breast cancer detection and risk management. Our analysis demonstrates that deep learning models achieve 90–99% accuracy across imaging modalities, with convolutional neural networks showing particular promise in mammography (99.96% accuracy) and ultrasound (100% accuracy) applications. Tabular data models using XGBoost achieve comparable performance (99.12% accuracy) for risk prediction. The study confirms that lifestyle modifications (dietary changes, BMI management, and alcohol reduction) significantly mitigate breast cancer risk. Key findings include the following: (1) hybrid models combining imaging and clinical data enhance early detection, (2) thermal imaging achieves high diagnostic accuracy (97–100% in optimized models) while offering a cost-effective, less hazardous screening option, (3) challenges persist in data variability and model interpretability. These results highlight the need for integrated diagnostic systems combining technological innovations with preventive strategies. The review underscores AI’s transformative potential in breast cancer diagnosis while emphasizing the continued importance of risk factor management. Future research should prioritize multi-modal data integration and clinically interpretable models. Full article

Background: The amount of data produced from biological experiments has increased geometrically, posing a challenge for the development of new methodologies that could enable their interpretation. We propose a novel approach for the analysis of transcriptomic data derived from acute lymphoblastic leukemia (ALL) and rhabdomyosarcoma (RMS) cell lines, using bioinformatics, systems biology and geometrical approaches. Methods: The expression profile of each cell line was investigated using microarrays, and identified genes were used to create a systems pathway model, which was then simulated using differential equations. The transcriptomic profile used involved genes with similar expression levels. The simulated results were further analyzed using geometrical approaches to identify common expressional dynamics. Results: We simulated and analyzed the system network using time series, regression analysis and helical functions, detecting predictable structures after iterating the modelled biological network, focusing on TIE1, STAT1, MAPK14 and ADAM17. Our results show that such common attributes in gene expression patterns can lead to more effective treatment options and help in the discovery of universal tumor biomarkers. Discussion: Our approach was able to identify complex structures in gene expression patterns, indicating that such approaches could prove useful towards the understanding of the complex tumor dynamics. Full article

Accurate segmentation of kidney microstructures in whole slide images (WSIs) is essential for the diagnosis and monitoring of renal diseases. In this study, an end-to-end instance segmentation pipeline was developed for the detection of glomeruli and blood vessels in hematoxylin and eosin (H&E) stained kidney tissue. A tiling-based strategy was employed using Slicing Aided Hyper Inference (SAHI) to manage the resolution and scale of WSIs and the performance of two segmentation models, YOLOv11 and YOLOv12, was comparatively evaluated. The influence of tile overlap ratios on segmentation quality and inference efficiency was assessed, with configurations identified that balance object continuity and computational cost. To address object fragmentation at tile boundaries, an Enhanced Syncretic Mask Merging algorithm was introduced, incorporating morphological and spatial constraints. The algorithm’s hyperparameters were optimized using Particle Swarm Optimization (PSO), with vessel and glomerulus-specific performance targets. The optimization process revealed key parameters affecting segmentation quality, particularly for vessel structures with fine, elongated morphology. When compared with a baseline without postprocessing, improvements in segmentation precision were observed, notably a 48% average increase for glomeruli and up to 17% for blood vessels. The proposed framework demonstrates a balance between accuracy and efficiency, supporting scalable histopathology analysis and contributing to the Vasculature Common Coordinate Framework (VCCF) and Human Reference Atlas (HRA). Full article

Skin cancer is the most common cancer worldwide, for which early detection is crucial to improve survival rates. Visual inspection and biopsies have limitations, including being error-prone, costly, and time-consuming. Although several deep learning models have been developed, they demonstrate significant limitations. An interpretable and improved transfer learning model for binary skin cancer classification is proposed in this research, which uses the last convolutional block of VGG16 as the feature extractor. The methodology focuses on addressing the existing limitations in skin cancer classification, to support dermatologists and potentially saving lives through advanced, reliable, and accessible AI-driven diagnostic tools. Explainable AI is incorporated for the visualization and explanation of classifications. Multiple optimization techniques are applied to avoid overfitting, ensure stable training, and enhance the classification accuracy of dermoscopic images into benign and malignant classes. The proposed model shows 90.91% classification accuracy, which is better than state-of-the-art models and established approaches in skin cancer classification. An interactive desktop application integrating the model is developed, enabling real-time preliminary screening with offline access. Full article

Background: Prostate cancer (PC) is the second leading cause of cancer-related death in men in the United States. A subset of patients develops non-metastatic, castration-resistant PC (nmCRPC), for which management requires a personalized consideration for appropriate treatment. However, there is no consensus regarding when to switch from androgen deprivation therapy (ADT) to more aggressive treatments like abiraterone or enzalutamide. Methods: We analyzed 5037 nmCRPC patients and employed a Weibull Time to Event Recurrent Neural Network to identify patients who would benefit from switching from ADT to abiraterone/enzalutamide. We evaluated this model using differential treatment benefits measured by the Kaplan–Meier estimation and milestone probabilities. Results: The model achieved an area under the curve of 0.738 (standard deviation (SD): 0.057) for patients treated with abiraterone/enzalutamide and 0.693 (SD: 0.02) for patients exclusively treated with ADT at the 2-year milestone. The model recommended 14% of ADT patients switch to abiraterone/enzalutamide. Analysis showed a statistically significant absolute improvement using model-recommended treatments in progression-free survival (PFS) of 0.24 (95% confidence interval (CI): 0.23–0.24) at the 2-year milestone (PFS rate increasing from 0.50 to 0.74) with a hazard ratio of 0.44 (95% CI: 0.39–0.50). Conclusions: Our model successfully identified nmCRPC patients who would benefit from switching to abiraterone/enzalutamide, demonstrating potential outcome improvements. Full article