Front Plant Sci. 2025 Feb 18;16:1539068. doi: 10.3389/fpls.2025.1539068. eCollection 2025.

ABSTRACT

Intelligent and accurate evaluation of KASP primer typing effect is crucial for large-scale screening of excellent markers in molecular marker-assisted breeding. However, the efficiency of both manual discrimination methods and existing algorithms is limited and cannot match the development speed of molecular markers. To address the above problems, we proposed a typing evaluation method for KASP primers by integrating deep learning and traditional machine learning algorithms, called TAL-SRX. First, three algorithms are used to optimize the performance of each model in the Stacking framework respectively, and five-fold cross-validation is used to enhance stability. Then, a hybrid neural network is constructed by combining ANN and LSTM to capture nonlinear relationships and extract complex features, while the Transformer algorithm is introduced to capture global dependencies in high-dimensional feature space. Finally, the two machine learning algorithms are fused through a soft voting integration strategy to output the KASP marker typing effect scores. In this paper, the performance of the model was tested using the KASP test results of 3399 groups of cotton variety resource materials, with an accuracy of 92.83% and an AUC value of 0.9905, indicating that the method has high accuracy, consistency and stability, and the overall performance is better than that of a single model. The performance of the TAL-SRX method is the best when compared with the different integrated combinations of methods. In summary, the TAL-SRX model has good evaluation performance and is very suitable for providing technical support for molecular marker-assisted breeding and other work.

PMID:40041015 | PMC:PMC11876144 | DOI:10.3389/fpls.2025.1539068

J Imaging Inform Med. 2025 Mar 4. doi: 10.1007/s10278-025-01458-x. Online ahead of print.

ABSTRACT

BACKGROUND: The increasing rates of lung cancer emphasize the need for early detection through computed tomography (CT) scans, enhanced by deep learning (DL) to improve diagnosis, treatment, and patient survival. This review examines current and prospective applications of 2D- DL networks in lung cancer CT segmentation, summarizing research, highlighting essential concepts and gaps; Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a systematic search of peer-reviewed studies from 01/2020 to 12/2024 on data-driven population segmentation using structured data was conducted across databases like Google Scholar, PubMed, Science Direct, IEEE (Institute of Electrical and Electronics Engineers) and ACM (Association for Computing Machinery) library. 124 studies met the inclusion criteria and were analyzed.

RESULTS: The LIDC-LIDR dataset was the most frequently used; The finding particularly relies on supervised learning with labeled data. The UNet model and its variants were the most frequently used models in medical image segmentation, achieving Dice Similarity Coefficients (DSC) of up to 0.9999. The reviewed studies primarily exhibit significant gaps in addressing class imbalances (67%), underuse of cross-validation (21%), and poor model stability evaluations (3%). Additionally, 88% failed to address the missing data, and generalizability concerns were only discussed in 34% of cases.

CONCLUSIONS: The review emphasizes the importance of Convolutional Neural Networks, particularly UNet, in lung CT analysis and advocates for a combined 2D/3D modeling approach. It also highlights the need for larger, diverse datasets and the exploration of semi-supervised and unsupervised learning to enhance automated lung cancer diagnosis and early detection.

PMID:40038137 | DOI:10.1007/s10278-025-01458-x

J Imaging Inform Med. 2025 Mar 4. doi: 10.1007/s10278-025-01449-y. Online ahead of print.

ABSTRACT

Accurate segmentation of adrenal glands from CT images is essential for enhancing computer-aided diagnosis and surgical planning. However, the small size, irregular shape, and proximity to surrounding tissues make this task highly challenging. This study introduces a novel pipeline that significantly improves the segmentation of left and right adrenal glands by integrating advanced pre-processing techniques and a robust post-processing framework. Utilising a 2D UNet architecture with various backbones (VGG16, ResNet34, InceptionV3), the pipeline leverages test-time augmentation (TTA) and targeted removal of unconnected regions to enhance accuracy and robustness. Our results demonstrate a substantial improvement, with a 38% increase in the Dice similarity coefficient for the left adrenal gland and an 11% increase for the right adrenal gland on the AMOS dataset, achieved by the InceptionV3 model. Additionally, the pipeline significantly reduces false positives, underscoring its potential for clinical applications and its superiority over existing methods. These advancements make our approach a crucial contribution to the field of medical image segmentation.

PMID:40038136 | DOI:10.1007/s10278-025-01449-y

J Am Med Inform Assoc. 2025 Mar 4:ocaf021. doi: 10.1093/jamia/ocaf021. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while maintaining high diagnostic accuracy.

MATERIALS AND METHODS: A private dataset of 2719 OCT images from 493 patients was employed, along with 3 public datasets comprising 84 484 images from 4686 patients, 3231 images from 45 patients, and 572 images. Extensive internal, external, and clinical validation were performed to assess model performance. Grad-CAM was employed for qualitative analysis to interpret the model's decisions by highlighting relevant areas. Subsampling analyses evaluated the model's robustness with varying labeled data availability.

RESULTS: The proposed model outperformed conventional supervised or self-supervised learning-based models, achieving state-of-the-art results across 3 public datasets. In a clinical validation, the model exhibited up to 17.50% higher accuracy and 17.53% higher macro F-1 score than a supervised learning-based model under limited training data.

DISCUSSION: The model's robustness in OCT image classification underscores the potential of the multi-stage self-supervised learning to address challenges associated with limited labeled data. The availability of source codes and pre-trained models promotes the use of this model in a variety of clinical settings, facilitating broader adoption.

CONCLUSION: This model offers a promising solution for advancing OCT image classification, achieving high accuracy while reducing the cost of extensive expert annotation and potentially streamlining clinical workflows, thereby supporting more efficient patient management.

PMID:40037789 | DOI:10.1093/jamia/ocaf021

J Orthop Surg Res. 2025 Mar 4;20(1):236. doi: 10.1186/s13018-025-05620-7.

ABSTRACT

BACKGROUND: Accurate measurement of the spinal alignment parameters is crucial for diagnosing and evaluating adolescent idiopathic scoliosis (AIS). Manual measurement is subjective and time-consuming. The recently developed artificial intelligence models mainly focused on measuring the coronal Cobb angle (CA) and ignored the evaluation of the sagittal plane. We developed a deep-learning model that could automatically measure spinal alignment parameters in biplanar radiographs.

METHODS: In this study, our model adopted ResNet34 as the backbone network, mainly consisting of keypoint detection and CA measurement. A total of 600 biplane radiographs were collected from our hospital and randomly divided into train and test sets in a 3:1 ratio. Two senior spinal surgeons independently manually measured and analyzed spinal alignment and recorded the time taken. The reliabilities of automatic measurement were evaluated by comparing them with the gold standard, using mean absolute difference (MAD), intraclass correlation coefficient (ICC), simple linear regression, and Bland-Altman plots. The diagnosis performance of the model was evaluated through the receiver operating characteristic (ROC) curve and area under the curve (AUC). Severity classification and sagittal abnormalities classification were visualized using a confusion matrix.

RESULTS: Our AI model achieved the MAD of coronal and sagittal angle errors was 2.15° and 2.72°, and ICC was 0.985, 0.927. The simple linear regression showed a strong correction between all parameters and the gold standard (p < 0.001, r2 ≥ 0.686), the Bland-Altman plots showed that the mean difference of the model was within 2° and the automatic measurement time was 9.1 s. Our model demonstrated excellent diagnostic performance, with an accuracy of 97.2%, a sensitivity of 96.8%, a specificity of 97.6%, and an AUC of 0.972 (0.940-1.000).For severity classification, the overall accuracy was 94.5%. All accuracy of sagittal abnormalities classification was greater than 91.8%.

CONCLUSIONS: This deep learning model can accurately and automatically measure spinal alignment parameters with reliable results, significantly reducing diagnostic time, and might provide the potential to assist clinicians.

PMID:40038733 | DOI:10.1186/s13018-025-05620-7

J Transl Med. 2025 Mar 4;23(1):267. doi: 10.1186/s12967-025-06234-7.

ABSTRACT

BACKGROUND: Homologous recombination deficiency (HRD) refers to the dysfunction of homologous recombination repair (HRR) at the cellular level. The assessment of HRD status has the important significance for the formulation of treatment plans, efficacy evaluation, and prognosis prediction of patients with ovarian cancer.

OBJECTIVES: This study aimed to construct a deep learning-based classifier for identifying tumor regions from whole slide images (WSIs) and stratify the HRD status of patients with ovarian cancer (OC).

METHODS: The deep learning models were trained on 205 H&E-stained sections which contained 205 ovarian cancer patients, 64 were found to have HRD status while 141 had homologous recombination proficiency (HRP) status from two institutions Memorial Sloan Kettering Cancer Center (MSKCC) and Zhongda Hospital, Southeast University. The framework includes tumor regions identification by UNet + + and subtypes of ovarian cancer classifier construction. Referring to the EasyEnsemble, we classified the HRP patients into three distributed subsets. These three subsets of HRP patients were combined with the HRD patients to establish three new training groups for subsequent model construction. The three models were integrated into a single model named Ensemble Model.

RESULTS: The UNet + + algorithm segmented tumor regions with 81.8% accuracy, 85.9% recall, 83.8% dice score and 68.3% IoU. The AUC of the Ensemble Model was 0.769 (Precision = 0.800, Recall = 0.727, F1-score = 0.762) in the study. The most discriminative features between HRD and HRP comprised S_mean_dln_obtuse_ratio, S_mean_dln_acute_ratio and mean_Graph_T-S_Betweenness_normed.

CONCLUSIONS: The models we constructed enables accurate discrimination between tumor and non-tumor tissues in ovarian cancer as well as the prediction of HRD status for patients with ovarian cancer.

PMID:40038690 | DOI:10.1186/s12967-025-06234-7

BMC Med Imaging. 2025 Mar 4;25(1):71. doi: 10.1186/s12880-025-01604-5.

ABSTRACT

BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effective treatment. Chest X-ray (CXR) has become the most common alternative method for detecting pulmonary diseases such as COVID-19, pneumonia, and lung opacity due to their availability, cost-effectiveness, and ability to facilitate comparative analysis. However, the interpretation of CXRs is a challenging task.

METHODS: This study presents an automated deep learning (DL) model that outperforms multiple state-of-the-art methods in diagnosing COVID-19, Lung Opacity, and Viral Pneumonia. Using a dataset of 21,165 CXRs, the proposed framework introduces a seamless combination of the Vision Transformer (ViT) for capturing long-range dependencies, DenseNet201 for powerful feature extraction, and global average pooling (GAP) for retaining critical spatial details. This combination results in a robust classification system, achieving remarkable accuracy.

RESULTS: The proposed methodology delivers outstanding results across all categories: achieving 99.4% accuracy and an F1-score of 98.43% for COVID-19, 96.45% accuracy and an F1-score of 93.64% for Lung Opacity, 99.63% accuracy and an F1-score of 97.05% for Viral Pneumonia, and 95.97% accuracy with an F1-score of 95.87% for Normal subjects.

CONCLUSION: The proposed framework achieves a remarkable overall accuracy of 97.87%, surpassing several state-of-the-art methods with reproducible and objective outcomes. To ensure robustness and minimize variability in train-test splits, our study employs five-fold cross-validation, providing reliable and consistent performance evaluation. For transparency and to facilitate future comparisons, the specific training and testing splits have been made publicly accessible. Furthermore, Grad-CAM-based visualizations are integrated to enhance the interpretability of the model, offering valuable insights into its decision-making process. This innovative framework not only boosts classification accuracy but also sets a new benchmark in CXR-based disease diagnosis.

PMID:40038588 | DOI:10.1186/s12880-025-01604-5

Nat Struct Mol Biol. 2025 Mar 4. doi: 10.1038/s41594-025-01512-w. Online ahead of print.

ABSTRACT

Differential high-order chromatin interactions between homologous chromosomes affect many biological processes. Traditional chromatin conformation capture genome analysis methods mainly identify two-way interactions and cannot provide comprehensive haplotype information, especially for low-heterozygosity organisms such as human. Here, we present a pipeline of methods to delineate diploid high-order chromatin interactions from noisy Pore-C outputs. We trained a previously published single-nucleotide variant (SNV)-calling deep learning model, Clair3, on Pore-C data to achieve superior SNV calling, applied a filtering strategy to tag reads for haplotypes and established a haplotype imputation strategy for high-order concatemers. Learning the haplotype characteristics of high-order concatemers from high-heterozygosity mouse allowed us to devise a progressive haplotype imputation strategy, which improved the haplotype-informative Pore-C contact rate 14.1-fold to 76% in the HG001 cell line. Overall, the diploid three-dimensional (3D) genome interactions we derived using Dip3D surpassed conventional methods in noise reduction and contact distribution uniformity, with better haplotype-informative contact density and genomic coverage rates. Dip3D identified previously unresolved haplotype high-order interactions, in addition to an understanding of their relationship with allele-specific expression, such as in X-chromosome inactivation. These results lead us to conclude that Dip3D is a robust pipeline for the high-quality reconstruction of diploid high-order 3D genome interactions.

PMID:40038455 | DOI:10.1038/s41594-025-01512-w

Sci Rep. 2025 Mar 4;15(1):7604. doi: 10.1038/s41598-025-92096-4.

ABSTRACT

Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes a methodology that combines the use of advanced medical imaging with predictive modeling for the improvement of burn injury assessment. The proposed framework makes use of the Adaptive Complex Independent Components Analysis (ACICA) and Reference Region (TBSA) methods in conjunction with deep learning techniques for the precise estimation of burn depth and Total Body Surface Area analysis. It also allows for the estimation of the depth of burns with high accuracy, calculation of TBSA, and non-invasive analysis with 96.7% accuracy using an RNN model. Extensive experimentation on DCE-LUV samples validates enhanced diagnostic precision and detailed texture analysis. These technologies provide nuanced insights into burn severity, improving diagnostic accuracy and treatment planning. Our results demonstrate the potential of these methods to revolutionize burn care and optimize patient outcomes.

PMID:40038450 | DOI:10.1038/s41598-025-92096-4

Sci Rep. 2025 Mar 4;15(1):7540. doi: 10.1038/s41598-025-91622-8.

ABSTRACT

In the rapidly evolving field of healthcare, Artificial Intelligence (AI) is increasingly driving the promotion of the transformation of traditional healthcare and improving medical diagnostic decisions. The overall goal is to uncover emerging trends and potential future paths of AI in healthcare by applying text mining to collect scientific papers and patent information. This study, using advanced text mining and multiple deep learning algorithms, utilized the Web of Science for scientific papers (1587) and the Derwent innovations index for patents (1314) from 2018 to 2022 to study future trends of emerging AI in healthcare. A novel self-supervised text mining approach, leveraging bidirectional encoder representations from transformers (BERT), is introduced to explore AI trends in healthcare. The findings point out the market trends of the Internet of Things, data security and image processing. This study not only reveals current research hotspots and technological trends in AI for healthcare but also proposes an advanced research method. Moreover, by analysing patent data, this study provides an empirical basis for exploring the commercialisation of AI technology, indicating the potential transformation directions for future healthcare services. Early technology trend analysis relied heavily on expert judgment. This study is the first to introduce a deep learning self-supervised model to the field of AI in healthcare, effectively improving the accuracy and efficiency of the analysis. These findings provide valuable guidance for researchers, policymakers and industry professionals, enabling more informed decisions.

PMID:40038367 | DOI:10.1038/s41598-025-91622-8

Sci Rep. 2025 Mar 4;15(1):7644. doi: 10.1038/s41598-025-90511-4.

ABSTRACT

Loop closure detection is a key module in visual SLAM. During the robot's movement, the cumulative error of the robot is reduced by the loop closure detection method, which can provide constraints for the back-end pose optimization, and the SLAM system can build an accurate map. Traditional loop closure detection algorithms rely on the bag-of-words model, which involves a complex process, has slow loading speeds, and is sensitive to changes in illumination or viewing angles. Therefore, aiming at the problems of traditional methods, this paper proposes an algorithm based on the Siamese capsule neural network by using the deep learning method. We have designed a new feature extractor for capsule networks, and in order to further reduce the parameter count, we have performed pruning based on the characteristics of the capsule layer. The algorithm was tested on the CityCentre dataset and the New College dataset. Our experimental results show that the proposed algorithm in this paper has higher accuracy and robustness compared to traditional methods and other deep learning methods. Our algorithm demonstrates good robustness under changes in illumination and viewing angles. Finally, we evaluated the performance of the complete SLAM system on the KITTI dataset.

PMID:40038350 | DOI:10.1038/s41598-025-90511-4

Sci Rep. 2025 Mar 4;15(1):7552. doi: 10.1038/s41598-025-90616-w.

ABSTRACT

The process of image formulation uses semantic analysis to extract influential vectors from image components. The proposed approach integrates DenseNet with ResNet-50, VGG-19, and GoogLeNet using an innovative bonding process that establishes algorithmic channeling between these models. The goal targets compact efficient image feature vectors that process data in parallel regardless of input color or grayscale consistency and work across different datasets and semantic categories. Image patching techniques with corner straddling and isolated responses help detect peaks and junctions while addressing anisotropic noise through curvature-based computations and auto-correlation calculations. An integrated channeled algorithm processes the refined features by uniting local-global features with primitive-parameterized features and regioned feature vectors. Using K-nearest neighbor indexing methods analyze and retrieve images from the harmonized signature collection effectively. Extensive experimentation is performed on the state-of-the-art datasets including Caltech-101, Cifar-10, Caltech-256, Cifar-100, Corel-10000, 17-Flowers, COIL-100, FTVL Tropical Fruits, Corel-1000, and Zubud. This contribution finally endorses its standing at the peak of deep and complex image sensing analysis. A state-of-the-art deep image sensing analysis method delivers optimal channeling accuracy together with robust dataset harmonization performance.

PMID:40038324 | DOI:10.1038/s41598-025-90616-w

Sci Rep. 2025 Mar 4;15(1):7558. doi: 10.1038/s41598-025-88184-0.

ABSTRACT

Traffic signs are pivotal components of traffic management, ensuring the regulation and safety of road traffic. However, existing detection methods often suffer from low accuracy and poor real-time performance in dynamic road environments. This paper reviews traditional traffic sign detection methods and introduces an enhanced detection algorithm (YOLO-BS) based on YOLOv8 (You Only Look Once version 8). This algorithm addresses the challenges of complex backgrounds and small-sized detection targets in traffic sign images. A small object detection layer was incorporated into the YOLOv8 framework to enrich feature extraction. Additionally, a bidirectional feature pyramid network (BiFPN) was integrated into the detection framework to enhance the handling of multi-scale objects and improve the performance in detecting small objects. Experiments were conducted on the TT100K dataset to evaluate key metrics such as model size, recall, mean average precision (mAP), and frames per second (FPS), demonstrating that YOLO-BS surpasses current mainstream models with mAP50 of 90.1% and FPS of 78. Future work will refine YOLO-BS to explore broader applications within intelligent transportation systems.

PMID:40038318 | DOI:10.1038/s41598-025-88184-0

Jpn J Radiol. 2025 Mar 5. doi: 10.1007/s11604-025-01756-y. Online ahead of print.

ABSTRACT

PURPOSE: To assess the utility of dual-type deep learning (DL)-based image reconstruction with DL-based image denoising and super-resolution processing by comparing images reconstructed with the conventional method in head and neck fat-suppressed (Fs) T2-weighted imaging (T2WI).

MATERIALS AND METHODS: We retrospectively analyzed the cases of 43 patients who underwent head/neck Fs-T2WI for the assessment of their head and neck lesions. All patients underwent two sets of Fs-T2WI scans with conventional- and DL-based reconstruction. The Fs-T2WI with DL-based reconstruction was acquired based on a 30% reduction of its spatial resolution in both the x- and y-axes with a shortened scan time. Qualitative and quantitative assessments were performed with both the conventional method- and DL-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, visibility of anatomical structures, degree of artifact(s), lesion conspicuity, and lesion edge sharpness based on five-point grading. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the lesion and the contrast-to-noise ratio (CNR) between the lesion and the adjacent or nearest muscle.

RESULTS: In the qualitative analysis, significant differences were observed between the Fs-T2WI with the conventional- and DL-based reconstruction in all of the evaluation items except the degree of the artifact(s) (p < 0.001). In the quantitative analysis, significant differences were observed in the SNR between the Fs-T2WI with conventional- (21.4 ± 14.7) and DL-based reconstructions (26.2 ± 13.5) (p < 0.001). In the CNR assessment, the CNR between the lesion and adjacent or nearest muscle in the DL-based Fs-T2WI (16.8 ± 11.6) was significantly higher than that in the conventional Fs-T2WI (14.2 ± 12.9) (p < 0.001).

CONCLUSION: Dual-type DL-based image reconstruction by an effective denoising and super-resolution process successfully provided high image quality in head and neck Fs-T2WI with a shortened scan time compared to the conventional imaging method.

PMID:40038217 | DOI:10.1007/s11604-025-01756-y

J Mech Behav Biomed Mater. 2025 Feb 25;166:106949. doi: 10.1016/j.jmbbm.2025.106949. Online ahead of print.

ABSTRACT

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. This article reviews the application of artificial intelligence (AI) to enhance pre-, during-, and post-AM processes to meet a wider range of subject-specific requirements of healthcare interventions. This article introduces common AM processes and AI tools, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of AI in pre-processing is described in the core dimensions like structural design and image reconstruction, material design and formulations, and processing parameters. The role of AI in a printing process is described based on hardware specifications, printing configurations, and core operational parameters such as temperature. Likewise, the post-processing describes the role of AI for surface finishing, dimensional accuracy, curing processes, and a relationship between AM processes and bioactivity. The later sections provide detailed scientometric studies, thematic evaluation of the subject topic, and also reflect on AI ethics in AM for biomedical applications. This review article perceives AI as a robust and powerful tool for AM of biomedical products. From tissue engineering (TE) to prosthesis, lab-on-chip to organs-on-a-chip, and additive biofabrication for range of products; AI holds a high potential to screen desired process-property-performance relationships for resource-efficient pre- to post-AM cycle to develop high-quality healthcare products with enhanced subject-specific compliance specification.

PMID:40036906 | DOI:10.1016/j.jmbbm.2025.106949

Gigascience. 2025 Jan 6;14:giaf008. doi: 10.1093/gigascience/giaf008.

ABSTRACT

BACKGROUND: Precise prediction of epitope presentation on human leukocyte antigen (HLA) molecules is crucial for advancing vaccine development and immunotherapy. Conventional HLA-peptide binding affinity prediction tools often focus on specific alleles and lack a universal approach for comprehensive HLA site analysis. This limitation hinders efficient filtering of invalid peptide segments.

RESULTS: We introduce TransHLA, a pioneering tool designed for epitope prediction across all HLA alleles, integrating Transformer and Residue CNN architectures. TransHLA utilizes the ESM2 large language model for sequence and structure embeddings, achieving high predictive accuracy. For HLA class I, it reaches an accuracy of 84.72% and an area under the curve (AUC) of 91.95% on IEDB test data. For HLA class II, it achieves 79.94% accuracy and an AUC of 88.14%. Our case studies using datasets like CEDAR and VDJdb demonstrate that TransHLA surpasses existing models in specificity and sensitivity for identifying immunogenic epitopes and neoepitopes.

CONCLUSIONS: TransHLA significantly enhances vaccine design and immunotherapy by efficiently identifying broadly reactive peptides. Our resources, including data and code, are publicly accessible at https://github.com/SkywalkerLuke/TransHLA.

PMID:40036690 | DOI:10.1093/gigascience/giaf008

J Econ Entomol. 2025 Feb 27:toaf035. doi: 10.1093/jee/toaf035. Online ahead of print.

ABSTRACT

Plant-specific insect scouting and prediction are still challenging in most crop systems. In this article, a machine-learning algorithm is proposed to predict populations during whiteflies (Bemisia tabaci, Hemiptera; Gennadius Aleyrodidae) scouting and aid in determining the population distribution of adult whiteflies in cotton plant canopies. The study investigated the main location of adult whiteflies relative to plant nodes (stem points where leaves or branches emerge), population variation within and between canopies, whitefly density variability across fields, the impact of dense nodes on overall canopy populations, and the feasibility of using machine learning for prediction. Daily scouting was conducted on 64 non-pesticide cotton plants, focusing on all leaves of a node with the highest whitefly counts. A linear mixed-effect model assessed distribution over time, and machine-learning model selection identified a suitable forecasting model for the entire canopy whitefly population. Findings showed that the top 3 to 5 nodes are key habitats, with a single node potentially accounting for 44.4% of the full canopy whitefly population. The Bagging Ensemble Artificial Neural Network Regression model accurately predicted canopy populations (R² = 85.57), with consistency between actual and predicted counts (P-value > 0.05). Strategic sampling of the top nodes could estimate overall plant populations when taking a few samples or transects across a field. The suggested machine-learning model could be integrated into computing devices and automated sensors to predict real-time whitefly population density within the entire plant canopy during scouting operations.

PMID:40036620 | DOI:10.1093/jee/toaf035

Bioinformatics. 2025 Feb 27:btaf092. doi: 10.1093/bioinformatics/btaf092. Online ahead of print.

ABSTRACT

MOTIVATION: Cryogenic Electron Microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them.

RESULTS: In this study, we introduce CryoTEN-a three-dimensional UNETR ++ style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1,295 cryo-EM maps as inputs and their corresponding simulated maps generated from known protein structures as targets. An independent test set containing 150 maps is used to evaluate CryoTEN, and the results demonstrate that it can robustly enhance the quality of cryo-EM density maps. In addition, automatic de novo protein structure modeling shows that protein structures built from the density maps processed by CryoTEN have substantially better quality than those built from the original maps. Compared to the existing state-of-the-art deep learning methods for enhancing cryo-EM density maps, CryoTEN ranks second in improving the quality of density maps, while running > 10 times faster and requiring much less GPU memory than them.

AVAILABILITY AND IMPLEMENTATION: The source code and data is freely available at https://github.com/jianlin-cheng/cryoten.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40036588 | DOI:10.1093/bioinformatics/btaf092

Genomics Proteomics Bioinformatics. 2025 Feb 27:qzaf011. doi: 10.1093/gpbjnl/qzaf011. Online ahead of print.

ABSTRACT

The rapid development of biological and medical examination methods has vastly expanded personal biomedical information, including molecular, cellular, image, and electronic health record datasets. Integrating this wealth of information enables precise disease diagnosis, biomarker identification, and treatment design in clinical settings. Artificial intelligence (AI) techniques, particularly deep learning models, have been extensively employed in biomedical applications, demonstrating increased precision, efficiency, and generalization. The success of the large language and vision models further significantly extends their biomedical applications. However, challenges remain in learning these multimodal biomedical datasets, such as data privacy, fusion, and model interpretation. In this review, we provided a comprehensive overview of various biomedical data modalities, multi-modal representation learning methods, and the applications of AI in biomedical data integrative analysis. Additionally, we discussed the challenges in applying these deep learning methods and how to better integrate them into biomedical scenarios. We then proposed future directions for adapting deep learning methods with model pre-training and knowledge integration to advance biomedical research and benefit their clinical applications.

PMID:40036568 | DOI:10.1093/gpbjnl/qzaf011

IEEE J Biomed Health Inform. 2025 Feb 20;PP. doi: 10.1109/JBHI.2025.3543907. Online ahead of print.

ABSTRACT

Prostate cancer is a critical healthcare challenge globally and is one of the most prevalent types of cancer in men. Early and accurate diagnosis is essential for effective treatment and improved patient outcomes. In the existing literature, computer-aided diagnosis (CAD) solutions have been developed to assist pathologists in various tasks, including classification, diagnosis, and prostate cancer grading. Content-based image retrieval (CBIR) techniques provide valuable approaches to enhance these computer-aided solutions. This study evaluates how generative deep learning models can improve the quality of retrievals within a CBIR system. Specifically, we propose applying a Siamese Network approach, which enables us to learn how to encode image patches into latent representations for retrieval purposes. We used the ProGleason-GAN framework trained on the SiCAPv2 dataset to create similar pairs of input patches. Our observations indicate that introducing synthetic patches leads to notable improvements in the evaluated metrics, underscoring the utility of generative models within CBIR tasks. Furthermore, this work is the first in the literature where latent representations optimized for CBIR are used to train an attention mechanism for performing Gleason Scoring of a WSI.

PMID:40036556 | DOI:10.1109/JBHI.2025.3543907