Computers in biology and medicine最新文献

{"title":"Deep Radon Prior: A fully unsupervised framework for sparse-view CT reconstruction","authors":"Shuo Xu ,&nbsp;Jintao Fu ,&nbsp;Yuewen Sun ,&nbsp;Peng Cong ,&nbsp;Xincheng Xiang","doi":"10.1016/j.compbiomed.2025.109853","DOIUrl":"10.1016/j.compbiomed.2025.109853","url":null,"abstract":"<div><h3>Background</h3><div>Sparse-view computed tomography (CT) substantially reduces radiation exposure but often introduces severe artifacts that compromise image fidelity. Recent advances in deep learning for solving inverse problems have shown considerable promise in enhancing CT reconstruction; however, most approaches heavily rely on high-quality training datasets and lack interpretability.</div></div><div><h3>Method</h3><div>To address these challenges, this paper introduces a novel, fully unsupervised deep learning framework that mitigates the dependency on extensive labeled data and improves the interpretability of the reconstruction process. Specifically, we propose the Deep Radon Prior (DRP) framework, inspired by the Deep Image Prior (DIP), which integrates a neural network as an implicit prior into the iterative reconstruction process. This integration facilitates the image domain and the Radon domain gradient feedback and progressively optimizes the neural network through multiple stages, effectively narrowing the solution space in the Radon domain for under-constrained imaging protocols.</div></div><div><h3>Results</h3><div>We discuss the convergence properties of DRP and validate our approach experimentally, demonstrating its ability to produce high-fidelity images while significantly reducing artifacts. Results indicate that DRP achieves comparable or superior performance to supervised methods, thereby addressing the inherent challenges of sparse-view CT and substantially enhancing image quality.</div></div><div><h3>Conclusions</h3><div>The introduction of DRP represents a significant advancement in sparse-view CT imaging by leveraging the inherent deep self-correlation of the Radon domain, enabling effective cooperation with neural network manifolds for image reconstruction. This paradigm shift toward fully unsupervised learning offers a scalable and insightful approach to medical imaging, potentially redefining the landscape of CT reconstruction.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FEGGNN: Feature-Enhanced Gated Graph Neural Network for robust few-shot skin disease classification","authors":"Abdulrahman Noman,&nbsp;Zou Beiji,&nbsp;Chengzhang Zhu,&nbsp;Mohammed Alhabib,&nbsp;Raeed Al-sabri","doi":"10.1016/j.compbiomed.2025.109902","DOIUrl":"10.1016/j.compbiomed.2025.109902","url":null,"abstract":"<div><div>Accurate and timely classification of skin diseases is essential for effective dermatological diagnosis. However, the limited availability of annotated images, particularly for rare or novel conditions, poses a significant challenge. Although few-shot learning (FSL) methods in computer-aided diagnosis (CAD) can decrease the dependence on extensive labeled data, their efficacy is often diminished by these challenges, particularly the catastrophic forgetting defect during the sequence of few-shot tasks. To address these challenges, we propose a Feature Enhanced Gated Graph Neural Network (FEGGNN) framework to improve the few-shot classification of skin diseases. The FEGGNN leverages an efficient Asymmetric Convolutional Network (ACNet) to extract high-quality feature maps from skin lesion images, which are subsequently used to construct a graph where nodes represent feature vectors and edges indicate similarities between samples. The core of FEGGNN consists of multiple aggregation blocks within the Graph Neural Network (GNN) framework, which iteratively refine node and edge features. Each block updates node features by aggregating information from neighboring nodes, weighted by edge features, to capture contextual relationships. Simultaneously, Gated Recurrent Units (GRUs) model long-term dependencies across tasks, enabling effective knowledge transfer and mitigating catastrophic forgetting. The Efficient Channel Attention (ECA) mechanism further enhances edge feature updates by focusing on the most relevant feature channels, optimizing edge weight computation. This iterative refinement process enables FEGGNN to progressively enhance feature representations, ensuring robust performance in diverse few-shot classification tasks. FEGGNN’s superior ability to generalize to unseen classes is demonstrated by its state-of-the-art performance, achieving 84.90% accuracy on Derm7pt and 95.19% on SD-198 in 2-way 5-shot settings.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the potential of direct-acting antivirals against Chikungunya virus through structure-based drug repositioning and molecular dynamic simulations","authors":"Nida Kalam ,&nbsp;Rafat Ali ,&nbsp;Vinod RMT Balasubramaniam","doi":"10.1016/j.compbiomed.2025.109989","DOIUrl":"10.1016/j.compbiomed.2025.109989","url":null,"abstract":"<div><div>The Chikungunya virus (CHIKV) represents a significant global health threat, particularly in tropical regions, and no FDA-approved antiviral treatments are currently available. This study investigates the potential of Direct-Acting Antivirals (DAAs) and protease inhibitors (PIs) that have been developed for the hepatitis C virus (HCV) in treating CHIKV. We analyzed the binding of eight HCV DAAs to the nsP2 protease of CHIKV, which is essential for viral replication. Our findings suggest repurposing hepatitis C virus (HCV) antivirals, specifically Simeprevir (SIM) and voxilaprevir (VOX), could be effective against CHIKV. Through computational analyses, we observed their strong binding affinity to CHIKV's nsP2 protease, indicating the promising potential of repositioning these drugs for CHIKV treatment. To validate the results of our computational study, we evaluated the antiviral efficacy of SIM and VOX in vitro, both as monotherapies and in combination with ribavirin (RIBA). Our findings revealed that DAAs exert a multifaced effect by targeting different stages of the CHIKV life cycle. Furthermore, the synergistic effects suggest that combining SIM and VOX with RIBA may provide a more effective therapeutic strategy than using either drug alone. Further research is necessary to optimize treatment protocols and improve outcomes for patients affected by CHIKV.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109989"},"PeriodicalIF":7.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Female autism categorization using CNN based NeuroNet57 and ant colony optimization","authors":"Adnan Ashraf ,&nbsp;Qingjie Zhao ,&nbsp;Waqas Haider Bangyal ,&nbsp;Mudassar Raza ,&nbsp;Mudassar Iqbal","doi":"10.1016/j.compbiomed.2025.109926","DOIUrl":"10.1016/j.compbiomed.2025.109926","url":null,"abstract":"<div><div>Autism identification and classification using biomedical medical image analysis has advanced recently. Research shows autistic females have different phenotypic and age-related brain variations than males. Gender-specific hormones and genes affect autistic female brain circuitry, unfortunately, female phenotypic and genotypic data is quite deficient. Since physicians spend much time in assessing autistic females manually. Advanced large-scale deep learning algorithms are in dire need of accurate medical diagnosis. This research proposed a 57-layer CNN architecture called NeuroNet57 that can extract features from fMRI factually. After pre-training on the Brain Tumour dataset, the NeuroNet57 model extracts female phenotypic features from autism brain imagining data exchange (ABIDE)-I+II datasets using T1 modality fMRI scans, resulting in feature matrices of 14372 × 4096 for ABIDE_I and 16168 × 4096 for ABIDE_II. Our model uses ant colony optimization (ACO) to select feature subsets for dimensionality reduction. Further, nine machine learning classifiers are used to categorize females with autism spectrum disorder (ASD) from females with control behavior. The KNN-based fineKNN (FKNN) classifier had 92.21% accuracy on ABIDE-I and 93.49% on ABIDE-II. This proves the effectiveness of our proposed model.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109926"},"PeriodicalIF":7.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering the role of TREM-1 in celiac disease: In silico insights into the recognition of gluten-derived peptides and inflammatory mechanisms","authors":"Amanda de Oliveira Matos ,&nbsp;Pedro Henrique dos Santos Dantas ,&nbsp;José Rodrigues do Carmo Neto ,&nbsp;Mike Telemaco Contreras Colmenares ,&nbsp;Andrei Giacchetto Felice ,&nbsp;Siomar de Castro Soares ,&nbsp;Marcelle Silva-Sales ,&nbsp;Helioswilton Sales-Campos","doi":"10.1016/j.compbiomed.2025.109981","DOIUrl":"10.1016/j.compbiomed.2025.109981","url":null,"abstract":"<div><h3>Background</h3><div>Celiac disease (CD) is a chronic enteropathy characterized by a permanent intolerance to gluten. While CD has been associated with heightened T cell responses and the involvement of distinct innate immunity components, the role of the triggering receptor expressed on myeloid cells (TREM) family in this disease remains unclear. Thus, as TREM-1 has already been implicated in other inflammatory bowel diseases, and given its role in the amplification of inflammation, we hypothesized that it might play a role in the pathophysiology of CD.</div></div><div><h3>Methods and results</h3><div>the STRING tool was used to predict protein-protein interaction networks between TREM-1 and CD signaling pathways. Then, molecular docking and molecular dynamics simulations were conducted to explore potential interactions between TREM-1 and different peptides derived from alpha-gliadin (25-mer, 33-mer and p31-43). Finally, we used transcriptomic data, available from public repositories, to assess <em>TREM1</em> gene expression, and genes involved in its signaling pathway, in CD patients. Our results found an association between TREM-1 and CD markers, with STRING analysis, and the <em>in silico</em> simulations suggesting that the receptor might recognize the alpha-gliadin peptides, with the TREM-1/p31-43 interaction as the most likely interaction to occur biologically. Furthermore, <em>TREM1</em> and its signaling pathway were increased in patients with active CD, while in those in clinical remission, the expression levels were similar to healthy controls.</div></div><div><h3>Conclusions</h3><div>collectively, our findings suggest that TREM-1 might recognize alpha-gliadin derived peptides, and TREM-1's activation may contribute to the intestinal inflammation observed in CD.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109981"},"PeriodicalIF":7.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive experimental and computational analysis of endemic Allium tuncelianum: Phytochemical profiling, antimicrobial activity, and In silico studies for potential therapeutic applications","authors":"Oğuzhan Özdemir ,&nbsp;Nurten Yılmaz ,&nbsp;Ahmad Badreddin Musatat ,&nbsp;Tuna Demirci ,&nbsp;Servet Çete ,&nbsp;Emrah Yerlikaya ,&nbsp;Mustafa Oğuzhan Kaya","doi":"10.1016/j.compbiomed.2025.109993","DOIUrl":"10.1016/j.compbiomed.2025.109993","url":null,"abstract":"<div><div><em>Allium tuncelianum</em> (TG), an endemic garlic species from Tunceli, Turkey, was investigated using a multidisciplinary approach combining experimental and computational methods. Density Functional Theory (DFT) calculations with B3LYP/def2-SVP/def2-TZVP basis sets were employed to analyze electronic properties, reactivity, and stability under gas and ethanol conditions. Headspace/GC-MS identified 10 major components, with diallyl disulfide (48.03 %) and 1-propene (20.72 %) as predominant. Antimicrobial assays revealed potent activity against MRSA, <em>Salmonella paratyphi</em> A, and <em>E. coli</em>, with MIC values as low as 0.063 mg/mL. Antioxidant capacity, evaluated via DPPH, metal chelating, and FRAP assays, showed promising results, with the water extract exhibiting the highest activity (1.74 mg BHT equivalent/mL). DFT and molecular docking studies highlighted key compounds as potential inhibitors of <em>E. coli</em> Gyrase B, with binding energies of −5.68 and −6.07 kcal/mol. ADME predictions indicated favorable drug-like properties, though some compounds showed potential CYP450 interactions and toxicity. This study provides a comprehensive understanding of TG's biochemical profile and therapeutic potential, offering insights for future research and optimization.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109993"},"PeriodicalIF":7.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs","authors":"Behrang Khaffafi ,&nbsp;Hadi Khoshakhalgh ,&nbsp;Mohammad Keyhanazar ,&nbsp;Ehsan Mostafapour","doi":"10.1016/j.compbiomed.2025.109938","DOIUrl":"10.1016/j.compbiomed.2025.109938","url":null,"abstract":"<div><h3>Background</h3><div>Computer-aided detection (CAD) systems have been widely used to assist medical professionals in interpreting medical images, aiding in the detection of potential diseases. Despite their usefulness, CAD systems cannot yet fully replace doctors in diagnosing many conditions due to limitations in current algorithms. Cerebral microbleeds (CMBs) are a critical area of concern for neurological health, and accurate detection of CMBs is essential for understanding their impact on brain function. This study aims to improve CMB detection by enhancing existing machine learning algorithms.</div></div><div><h3>Methods</h3><div>This paper presents four CNN-based algorithms designed to enhance CMB detection. The detection methods are categorized into traditional machine learning approaches and deep learning-based methods. The traditional methods, while computationally efficient, offer lower sensitivity, while CNN-based approaches promise greater accuracy. The algorithms proposed in this study include a multi-channel CNN with optimized architecture and a multiscale CNN structure, both of which were designed to reduce false positives and improve overall performance.</div></div><div><h3>Results</h3><div>The first CNN algorithm, with an optimized multi-channel architecture, demonstrated a sensitivity of 99.6 %, specificity of 99.3 %, and accuracy of 99.5 %. The fourth algorithm, based on a stable multiscale CNN structure, achieved sensitivity of 98.2 %, specificity of 97.4 %, and accuracy of 97.8 %. Both algorithms exhibited a significant reduction in false positives compared to traditional methods. The experiments conducted confirm the effectiveness of these algorithms in improving the precision and reliability of CMB detection.</div></div><div><h3>Conclusion</h3><div>The proposed CNN-based algorithms demonstrate a significant advancement in the automated detection of CMBs, with notable improvements in sensitivity, specificity, and accuracy. These results underscore the potential of deep learning models, particularly CNNs, in enhancing CAD systems for neurological disease detection and reducing diagnostic errors. Further research and optimization may allow these algorithms to be integrated into clinical practices, providing more reliable support for healthcare professionals.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109938"},"PeriodicalIF":7.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating local and global attention mechanisms for enhanced oral cancer detection and explainability","authors":"Syed Jawad Hussain Shah ,&nbsp;Ahmed Albishri ,&nbsp;Rong Wang ,&nbsp;Yugyung Lee","doi":"10.1016/j.compbiomed.2025.109841","DOIUrl":"10.1016/j.compbiomed.2025.109841","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Early detection of Oral Squamous Cell Carcinoma (OSCC) improves survival rates, but traditional diagnostic methods often produce inconsistent results. This study introduces the Oral Cancer Attention Network (OCANet), a U-Net-based architecture designed to enhance tumor segmentation in hematoxylin and eosin (H&amp;E)-stained images. By integrating local and global attention mechanisms, OCANet captures complex cancerous patterns that existing deep-learning models may overlook. A Large Language Model (LLM) analyzes feature maps and Grad-CAM visualizations to improve interpretability, providing insights into the model’s decision-making process.</div></div><div><h3>Methods:</h3><div>OCANet incorporates the Channel and Spatial Attention Fusion (CSAF) module, Squeeze-and-Excitation (SE) blocks, Atrous Spatial Pyramid Pooling (ASPP), and residual connections to refine feature extraction and segmentation. The model was evaluated on the Oral Cavity-Derived Cancer (OCDC) and Oral Cancer Annotated (ORCA) datasets and the DigestPath colon tumor dataset to assess generalizability. Performance was measured using accuracy, Dice Similarity Coefficient (DSC), and mean Intersection over Union (mIoU), focusing on class-specific segmentation performance.</div></div><div><h3>Results:</h3><div>OCANet outperformed state-of-the-art models across all datasets. On ORCA, it achieved 90.98% accuracy, 86.14% DSC, and 77.10% mIoU. On OCDC, it reached 98.24% accuracy, 94.09% DSC, and 88.84% mIoU. On DigestPath, it demonstrated strong generalization with 84.65% DSC despite limited training data. The model showed superior carcinoma detection performance, distinguishing cancerous from non-cancerous regions with high specificity.</div></div><div><h3>Conclusion:</h3><div>OCANet enhances tumor segmentation accuracy and interpretability in histopathological images by integrating advanced attention mechanisms. Combining visual and textual insights, its multimodal explainability framework improves transparency while supporting clinical decision-making. With strong generalization across datasets and computational efficiency, OCANet presents a promising tool for oral and other cancer diagnostics, particularly in resource-limited settings.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109841"},"PeriodicalIF":7.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection","authors":"Hatice Catal Reis ,&nbsp;Veysel Turk","doi":"10.1016/j.compbiomed.2025.109916","DOIUrl":"10.1016/j.compbiomed.2025.109916","url":null,"abstract":"<div><div>Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of experts in this field, the personal assessment process, the clinical workload, and the high level of similarity in disease classes make it difficult. In recent years, deep learning-based artificial intelligence models have shown promise, with the potential to increase diagnosis speed and accuracy. These models attract attention with their automatic learning and adaptation capabilities. In this study, the deep learning-based PADBSRNet model and the PADBSRNet-Vision Transformer (ViT) hybrid method are proposed for the detection of brain tumors and skin and lung cancers. PADBSRNet is a comprehensive deep neural network architecture that integrates separable and traditional convolution layers, multiple attention mechanisms, bidirectional recurrent neural networks, and cross-connections/multi-stage feature fusion strategies. This architecture offers significant advantages in terms of effectively extracting local-global, contextual features and accurately modeling long-term dependencies in image classification tasks. The second proposed approach developed a hybrid method that combines the advantages of the PADBSRNet model and the ViT model. Experimental analysis on medical datasets such as the Figshare Brain Tumor Dataset, IQ-OTH/NCCD Dataset, and Skin Cancer: Malignant vs. Benign Dataset has evaluated the proposed models' performances. According to the experimental results, the PADBSRNet model has shown successful performance with 95.24 %, 99.55 %, and 88.61 % accuracy rates, respectively. The experimental findings show that the proposed deep learning model can effectively learn the complex relationships and hidden patterns of cancer disease, thus producing applicable and effective results in cancer diagnosis.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109916"},"PeriodicalIF":7.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An extensive review on infectious disease diagnosis using machine learning techniques and next generation sequencing: State-of-the-art and perspectives","authors":"Javed Aalam,&nbsp;Syed Naseer Ahmad Shah,&nbsp;Rafat Parveen","doi":"10.1016/j.compbiomed.2025.109962","DOIUrl":"10.1016/j.compbiomed.2025.109962","url":null,"abstract":"<div><div>Infectious diseases, including tuberculosis (TB), HIV/AIDS, and emerging pathogens like COVID-19 pose severe global health challenges due to their rapid spread and significant morbidity and mortality rates. Next-generation sequencing (NGS) and machine learning (ML) have emerged as transformative technologies for enhancing disease diagnosis and management.</div></div><div><h3>Objective</h3><div>This review aims to explore integrating ML techniques with NGS for diagnosing infectious diseases, highlighting their effectiveness and identifying existing challenges.</div></div><div><h3>Methods</h3><div>A comprehensive literature review spanning the past decade was conducted using reputable databases, including IEEE Xplore, PubMed, Scopus, SpringerLink, and Science Direct. Research papers, articles, and conference proceedings meeting stringent quality criteria were analysed to assess the performance of ML algorithms applied to NGS and metagenomic NGS (mNGS) data.</div></div><div><h3>Results</h3><div>The findings reveal that ML algorithms, such as deep neural networks (DNNs), support vector machines (SVM), and K-nearest neighbours (KNN), achieve high accuracy rates, often exceeding 95 %, in diagnosing infectious diseases. Deep learning methods excel in genomic and metagenomic data analysis, while traditional algorithms like Gaussian mixture models (GMM) also demonstrate robust classification capabilities. Challenges include reliance on single data types and difficulty distinguishing closely related pathogens.</div></div><div><h3>Conclusion</h3><div>The integration of ML and NGS significantly advances infectious disease diagnosis, offering rapid and precise detection capabilities. Addressing current limitations can further enhance the effectiveness of these technologies, ultimately improving global public health outcomes.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"189 ","pages":"Article 109962"},"PeriodicalIF":7.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}