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BioMedInformatics
Scopus
ISSN 2673-7426 · MDPI AG · 351 articles in the index
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Lightweight Depression Detection Using 3D Facial Landmark Pseudo-Images and CNN-LSTM on DAIC-WOZ and E-DAIC
Background: Depression is a common mental disorder, and early and objective diagnosis of depression is challenging. New advances in deep learning show promise for processing audio and video content when screening for depression. Nevertheless, the majority of current methods rely on raw video…
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Toward Clinically Dependable AI for Brain Tumors: A Unified Diagnostic–Prognostic Framework and Triadic Evaluation Model
Artificial intelligence (AI) has shown promising performance in brain tumor diagnosis and prognosis; however, most reported advances remain difficult to translate into clinical practice due to limited interpretability, inconsistent evaluation protocols, and weak generalization across datasets and…
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Software Applications in Biomedicine: A Narrative Review of Translational Pathways from Data to Decision
Background/Objectives: Software is now core infrastructure in biomedical science, yet fragmented workflows across subfields hinder reproducibility and delay the translation of data into actionable decisions. There is a critical need for a cross-disciplinary synthesis to bridge these silos and…
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Development of Machine Learning Models to Predict 28-Day Mortality in Patients with Sepsis-Associated Liver Injury
Background: Sepsis-associated liver injury (SALI) is a serious complication of sepsis that increases the risk of organ dysfunction and mortality; however, early identification of high-risk patients remains difficult due to nonspecific clinical features and complex pathophysiology. This study aimed…
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AdaDenseNet-LUC: Adaptive Attention DenseNet for Laryngeal Ultrasound Image Classification
Evaluating the difficulty of endotracheal intubation during pre-anesthesia assessment has consistently posed a challenge for clinicians. Accurate prediction of intubation difficulty is crucial for subsequent treatment planning. However, existing diagnostic methods often suffer from low accuracy. To…
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Conditional Tabular Generative Adversarial Network Based Clinical Data Augmentation for Enhanced Predictive Modeling in Chronic Kidney Disease Diagnosis
The lack of clinical data for chronic kidney disease (CKD) prediction frequently results in model overfitting and inadequate generalization to novel samples. This research mitigates this constraint by utilizing a Conditional Tabular Generative Adversarial Network (CTGAN) to enhance a constrained…
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A Systems Biology and Artificial Intelligence Approach to Unveil Brigatinib’s Pharmacological Mechanism in Brain Metastases in ALK+ Non-Small Cell Lung Cancer
Background/Objectives: Brain metastases (BM) are a major challenge in the treatment of non-small cell lung cancer (NSCLC), particularly among patients with anaplastic lymphoma kinase rearrangements (ALK+ NSCLC), where incidence can reach up to 60% during the course of the disease. This study used…
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Recent Progress in Deep Learning for Chest X-Ray Report Generation
Chest X-ray radiology report generation is a challenging task that involves techniques from medical natural language processing and computer vision. This paper provides a comprehensive overview of recent progress. The annotation protocols, structure, linguistic characteristics, and size of the main…
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AUC-Proportional Dempster–Shafer Fusion for Uncertainty-Aware Survival Prediction in Diffuse Large B-Cell Lymphoma
Background: Accurate prognosis in diffuse large B-cell lymphoma (DLBCL) is limited by biological heterogeneity and the absence of formal per-patient uncertainty quantification for treatment-response prediction. This study introduces a multi-layer evidence fusion framework combining gene expression…
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Leakage-Controlled Machine Learning for Territorial Epidemiological Risk Stratification: A Biomedical Informatics Framework Using Administrative Health Data in the Colombian Orinoquía
Digital health observatories require predictive frameworks that transform administrative health data into reliable territorial risk indicators. We developed a biomedical informatics framework for machine-learning-based epidemiological risk stratification in the Colombian Orinoquía, with explicit…
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Auditing Data Leakage and Temporal Misalignment in Machine-Learning Prognosis of Breast Cancer: A Reproducible Censoring-Aware Reanalysis
Background: Near-perfect machine-learning performance on public clinical datasets can arise from data leakage or predictors unavailable at the intended prediction time. We audited a breast cancer vital-status workflow and established a censoring-aware benchmark. Methods: The public SEER-derived…
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Analysis of CNN-Based Deep Learning Architectures and Performance Enhancement Strategies for Pneumonia Classification Using Chest X-Ray Images
Background: Deep learning models, particularly convolutional neural networks (CNNs), have shown promising performance for pneumonia detection using chest X-ray images. However, the impact of preprocessing, architecture selection, data augmentation, and ensemble strategies has not been…
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KazRNA-Pipe-GPU-Accelerated, Reproducible Nextflow Workflow for Integrated Bulk and Single-Cell Transcriptomic Profiling
Reproducible high-performance computing pipelines for transcriptomic analysis are essential for population-scale precision oncology, yet most published workflows address only a single sequencing modality or lack benchmarking on underrepresented populations. We present KazRNA-Pipe, an open-source…
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DrugLM: A Unified Framework to Enhance Drug–Target Interaction Prediction by Incorporating Textual Embeddings via Language Models
Accurate prediction of drug–target interactions (DTIs) is a cornerstone of computational drug discovery, with the potential to reduce experimental costs and accelerate therapeutic development. Although deep learning methods such as graph neural networks and Transformers have achieved notable…
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DFU-MambaKAN: A Lightweight Hybrid Mamba-KAN Architecture for Diabetic Foot Ulcer Screening and Severity Grading
Diabetic foot ulcers (DFUs) are a major cause of lower-extremity amputation, and image-based decision support may assist screening and preliminary triage where specialist access is limited. We propose DFU-MambaKAN, a hybrid architecture combining a pure-PyTorch selective state-space (Mamba) block…
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From Explainability to Clinical Actionability in Multi-Modal AI for Cardiovascular Prediction: A Systematic Review
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, driving the need for reliable tools for early risk prediction. Artificial intelligence (AI) applied to electrocardiograms (ECGs) has shown strong predictive performance for future cardiac events, yet its clinical…
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In Silico Characterization of the Venom-Derived LW-9 Peptide Using PreADMET-Based Predictions
The LW-9 peptide was previously identified through screening of purified molecules from the venom of the spider Phoneutria nigriventer and has been described as an immunomodulatory compound with potential application in cancer therapy. The molecule was biochemically characterized and subjected to…
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Deep Learning-Based Multi-Class Body Fluid Cell Type Classification: A Comparative Evaluation of Image Enhancement Techniques
Accurate classification of cells from body fluid specimens can support cytological analysis, but microscopic images often present challenges such as low contrast, unclear boundaries, staining variation, class imbalance, and overlapping morphology. This study evaluated the impact of image…
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Skin Lesion Classification in Low-Resource Settings Using Lightweight CNNs with Uncertainty Estimation
Dermoscopic skin lesion classification is a task of major clinical importance but is computationally expensive, making it inaccessible in resource-constrained healthcare settings. In this paper, we introduce a computationally efficient skin lesion classification framework for seven classes using…
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Computational Phenotyping of Autism-Related Behaviors: A Cross-Cultural Machine Learning Study in Bangladesh
Background: Digital behavioral phenotyping of autism spectrum disorder (ASD) offers a promising approach for developing more scalable diagnostic frameworks across diverse global contexts. Machine learning (ML) models show promise for ASD diagnosis using behavioral videos, but critical questions…