Biodata Mining最新文献

{"title":"Tree-based ensemble learning models for protein-protein interactions detection: a review and experimental evaluation.","authors":"Kamal Taha","doi":"10.1186/s13040-025-00501-5","DOIUrl":"10.1186/s13040-025-00501-5","url":null,"abstract":"<p><strong>Motivation: </strong>Protein-protein interactions (PPIs) are central to many biological processes, influencing cellular functions and offering potential therapeutic insights. The increasing availability of genomic and proteomic data has propelled computational approaches, particularly machine learning (ML), to the forefront of PPI prediction, addressing limitations of traditional experimental methods such as scalability and cost. This paper presents an in-depth review of modern ensemble learning models for PPI prediction, specifically focusing on XGBoost, Gradient Boosting, LightGBM, and Random Forest. Ensemble learning models are particularly noteworthy for their ability to surpass traditional approaches by leveraging the combined strengths of multiple base learners. Unlike previous reviews that often concentrate on theoretical comparisons or a limited selection of methods, this study offers a balanced analysis that includes both empirical findings and experimental evaluations. The review examines recent advancements in the field and assesses these models based on critical factors such as scalability, interpretability, accuracy, and efficiency, providing a structured framework to highlight nuanced differences among the techniques.</p><p><strong>Scientific contribution: </strong>The article evaluates several ML techniques for PPI detection, with LightGBM and XGBoost emerging as the most effective methods due to their high accuracy and computational efficiency, particularly for large and complex datasets. Gradient Boosting and Random Forest also demonstrate strong performance, with Gradient Boosting excelling in capturing non-linear relationships and Random Forest offering robust interpretability. LightGBM stands out for its scalability and efficiency, while XGBoost benefits from built-in regularization to reduce overfitting.</p><p><strong>Results: </strong>Experimental evaluations across three benchmark datasets-DIP, HPRD, and STRING-demonstrated that LightGBM consistently achieved the highest performance among all models, with average accuracy reaching up to 86%, and top sensitivity, specificity, and precision scores. XGBoost followed closely, showing strong generalization and robustness due to its regularization capabilities. Gradient Boosting provided competitive accuracy but lagged slightly in computational efficiency. Random Forest, while the most interpretable, showed comparatively lower sensitivity but maintained solid precision, highlighting its strength in minimizing false positives. Overall, LightGBM and XGBoost outperformed other methods in handling large, complex, and imbalanced PPI datasets. These results affirm the suitability of advanced ensemble learning techniques for enhancing PPI prediction and offer practical guidance on selecting models based on performance priorities and application constraints.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"1"},"PeriodicalIF":6.1,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12771752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145641705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatics analysis of Rickettsia typhi autoimmune associations and screening of Streptomyces-derived inhibitors.","authors":"Zarrin Basharat, Hanan A Ogaly, Fatimah A M Al-Zahrani, Calvin R Wei, Syed Shah Hassan, Muhammad Salman, Madiha Islam, Ibrar Ahmed, Yasir Waheed, Seil Kim","doi":"10.1186/s13040-025-00499-w","DOIUrl":"10.1186/s13040-025-00499-w","url":null,"abstract":"<p><strong>Background: </strong>Rickettsia typhi is the causative agent of epidemic murine typhus and Rocky Mountain spotted fever. The infection can affect multiple vital organs, including the heart, lungs, kidneys, and brain. Doxycycline is the recommended treatment but inflammation, mal-response, and drug resistance may arise. No natural product inhibitors have been reported against this bacterium.</p><p><strong>Aim: </strong>The objective of this study was to establish a potential connection between autoimmune disorders triggered by R. typhi, identify therapeutic targets within its core proteome, and explore novel natural product inhibitors from Streptomyces spp. that could potentially inhibit it.</p><p><strong>Methodology: </strong>Complete proteomes of four publicly available R. typhi strains were used for pan-proteomic analysis. The fni gene product (Isopentenyl pyrophosphate isomerase) was selected as the potential drug target. Molecular docking of 607 Streptomyces-derived metabolites was performed, with top hits validated using DiffDock and Vinardo scoring. Additionally, the Absorption, Distribution, Metabolism, Excretion, and Toxicity properties of the leading compounds were assessed via pkCSM, and formulation characteristics optimized using FormulationAI.</p><p><strong>Results: </strong>Out of the 803 core proteins, associations between 14 proteins were mined for autoimmune diseases (including psoriasis, rheumatoid arthritis, optic atrophy, uveitis, even-plus syndrome, Sjogren syndrome, inflammatory bowel disease, allergic rhinitis, systemic lupus erythematosus, sclerosis, Stevens-Johnson syndrome, toxic epidermal necrolysis, colitis etc.). 17 core proteins were predicted as druggable. ZINC01482946 demonstrated the strongest inhibitory potential, as confirmed by DiffDock scoring, convolutional neural network-based ranking, and Vinardo scoring. It demonstrated a stable configuration and exhibited a favorable pharmacokinetic profile, with bioavailability enhanced through cyclodextrin complexation.</p><p><strong>Conclusion: </strong>To the best of our knowledge, this is the first report identifying human autoimmune associations with R. typhi and natural product inhibitors targeting the pathogen. ZINC01482946 shows potential as an effective inhibitor of R. typhi, while SBE-β-CD appears to be a promising cyclodextrin for improving its solubility and bioavailability.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"83"},"PeriodicalIF":6.1,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12661794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145641766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FARFOOD: a database of potential interactions between food compounds and drugs.","authors":"Ana María Nevado-Bulnes, Dixan Agustin Benitez, Guadalupe Cumplido-Laso, Jose María Carvajal-González, Sonia Mulero-Navarro, Angel-Carlos Román","doi":"10.1186/s13040-025-00493-2","DOIUrl":"10.1186/s13040-025-00493-2","url":null,"abstract":"<p><p>Nutrition is a fundamental aspect in human life development, both from a socio-economic and medical perspective. In recent years, new personalized approaches have been added to the biochemical study of nutrients. These approaches consider both the effect of foods on the body and the role of genes in metabolizing or digesting different nutrients. Drug-food interactions have been known for decades, and databases like FooDrugs include curated interactions from multiple sources. In this paper we develop a method to predict potential interactions between foods and drugs based on the structural similarity between food compounds and medications. Our results have produced a database and an app to consult potential interactions between drugs and foods that we have called FARFOOD. Additionally, we validated two of these potential interactions with widely used drugs (lisinopril and bupropion) through structural docking between the ligand protein and the food compounds that are structurally similar to the drug. Moreover, patient surveys are used in the lisinopril and bupropion cases in addition to allopurinol to assess the possible effects of the potentially interacting foods on the symptoms of the conditions for which the medication is prescribed. In summary, this manuscript presents an interesting computational resource for predictive food-drug interaction analysis.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"82"},"PeriodicalIF":7.9,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12645732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145606979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs).","authors":"Chhaya Gupta, Nasib Singh Gill, Preeti Gulia, Sangeeta Duhan, Noha Alduaiji, Piyush Kumar Shukla, Abhishek Dwivedi","doi":"10.1186/s13040-025-00497-y","DOIUrl":"10.1186/s13040-025-00497-y","url":null,"abstract":"<p><p>Plant leaf diseases (PLDs) can continue to be a significant problem in the agricultural sector, leading to significant losses in production and jeopardizing food security. Early detection is essential, and recent achievements in the domain of deep learning (DL) have made automated high-accuracy solutions possible. The most popular and commonly used of these is the You Only Look Once (YOLO) family of object detection models, which have been proposed to detect plant diseases in real time. This review presents a new and in-depth synthesis of YOLO-based methods, including YOLOv1 to YOLOv10 and the domain-specific variants, including CTB-YOLO (coriander), BED-YOLO (YOLOv10n), and RAG-augmented YOLOv8 (coffee). This work compares to previous surveys in that (i) it presents a structured dataset catalog containing information on size, resolution, disease classes, and limitations (such as imbalance and annotation problems); (ii) it provides comparative benchmarking analysis of performance measures (accuracy, precision, recall, F1-score, mean Average Precision, and frames per second) across versions of YOLO to illustrate trade-offs between speed and accuracy; and (iii) it gives forward-looking discussion on how (ii) open challenges and (iii) future research directions, including lightweight YOLO models to run on mobile. This review presents a summative reference and a new contribution to the progress of the YOLO-based PLD detection approach to sustainable agriculture.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"91"},"PeriodicalIF":6.1,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12750877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145597894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable machine learning model for predicting postoperative cholangitis in pediatric surgical patients with pancreaticobiliary maljunction.","authors":"Hui-Min Mao, Jia Geng, Bin Zhu, Shun-Gen Huang, Wan-Liang Guo","doi":"10.1186/s13040-025-00491-4","DOIUrl":"10.1186/s13040-025-00491-4","url":null,"abstract":"<p><p>PURPOSE: To develop and validate an explainable machine learning (ML) model to predict postoperative cholangitis (POC) in pediatric patients with pancreaticobiliary maljunction (PBM) using readily accessible clinical data. METHODS: We analyzed 337 children with PBM who underwent surgery, dividing them into training (n = 243, center I) and testing (n = 94, center II) sets. Six ML algorithms were applied, and the best-performing model was identified by area under the receiver operating characteristic curves (ROC-AUC) and precision-recall curves (PR-AUC). Model calibration, clinical applicability, and interpretability were further evaluated using calibration curves, decision curve analysis (DCA), and Shapley Additive Explanations (SHAP). RESULTS: After a median follow-up of 21.8 months, 13.2% (32/243) of patients from center I and 14.9% (14/94) from center II developed POC. The final random forest (RF) model exhibited the best performance, with ROC-AUC of 0.890 and PR-AUC of 0.764 in testing set, with good calibration across both sets. DCA confirmed that the final RF model was clinically useful. Nine key features were identified and ranked using SHAP analysis, with cholangial inflammatory infiltration and diameter of common bile duct being the most important. CONCLUSION: This explainable ML model could effectively predict POC, aiding clinicians in identifying high-risk patients and supporting individualized management in PBM. </p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"81"},"PeriodicalIF":7.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12628564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing accuracy and dimensionality: a swarm intelligence strategy for robust cancer genomics classification.","authors":"Abrar Yaqoob, Mushtaq Ahmad Mir, R Vijaya Lakshmi, Tejaswini Pradhan, G V V Jagannadha Rao, Ghanshyam G Tejani, Mohd Asif Shah","doi":"10.1186/s13040-025-00503-3","DOIUrl":"10.1186/s13040-025-00503-3","url":null,"abstract":"<p><p>High-dimensional gene expression datasets pose a major challenge in cancer classification due to redundancy, noise, and the risk of overfitting. To address these issues, this study proposes a hybrid framework that integrates the Dung Beetle Optimizer (DBO) for feature selection with Support Vector Machines (SVM) for classification. DBO, a recently developed nature-inspired algorithm, effectively identifies informative and non-redundant subsets of genes by simulating dung beetles' foraging, rolling, obstacle avoidance, stealing, and breeding behaviors. The selected features are then classified using SVM with Radial Basis Function (RBF) kernels, which provide robust decision boundaries even in high-dimensional spaces. Extensive experiments were conducted on publicly available cancer-related gene expression datasets, covering binary, ternary, and quaternary classification tasks. Results show that the proposed DBO-SVM framework achieves 97.4-98.0% accuracy on binary datasets and 84-88% accuracy on multiclass datasets, with balanced Precision, Recall, and F1-scores. These findings highlight the method's ability to enhance classification performance while reducing computational cost and improving biological interpretability. The proposed hybrid model demonstrates strong potential as an efficient and reliable tool for precision medicine and biomedical data analysis.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"79"},"PeriodicalIF":6.1,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12625619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational prediction of cellular elastic modulus from mechanosensitive gene expression at multiple biological levels.","authors":"Yanhong Xiong, Xiaoyan Zhou, Qi Wang","doi":"10.1186/s13040-025-00496-z","DOIUrl":"10.1186/s13040-025-00496-z","url":null,"abstract":"<p><strong>Background: </strong>Understanding cellular mechanical properties is crucial for investigating cell fate determination, embryonic development, and disease progression. Traditional methods of measuring cellular mechanical properties, such as atomic force microscopy, are time-consuming, labor-intensive, and low-throughput. Computational models which can capture the relationship between mechanosensitive gene expression, a readily accessible and cost-effective data source, and cellular mechanical properties offer promising alternatives.</p><p><strong>Results: </strong>In this study, we identified mechanosensitive genes from 104 cell lines, using RNA-seq data and corresponding elastic modulus from the MechanoBase database. Several statistical learning models were tested and gradient boosting regression emerged as the most effective, outperforming other models in accuracy. We termed this model MechanoGEPred. The model demonstrated its ability to predict elastic modulus variations across tissue samples, single cells, and tissue spatial domains, capturing complex relationships between gene expression and mechanical properties.</p><p><strong>Conclusions: </strong>By enabling predictions at multiple biological levels, MechanoGEPred offers a useful framework for inferring cellular elastic modulus directly from gene expression data. The model reveals biologically meaningful patterns and context-dependent differences, suggesting potential applications in biomechanics and cancer research, and providing a proof of concept for studying mechanical heterogeneity and its role in health and disease.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"80"},"PeriodicalIF":6.1,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12625330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From prompt engineering to agent engineering: expanding the AI toolbox with autonomous agentic AI collaborators for biomedical discovery.","authors":"Jason H Moore, Nicholas P Tatonetti","doi":"10.1186/s13040-025-00502-4","DOIUrl":"10.1186/s13040-025-00502-4","url":null,"abstract":"","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"78"},"PeriodicalIF":6.1,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12613637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145514505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CAUSALRLSTACK: adaptive balancing of deep representation and causal effect estimation with application to HIV-related health data.","authors":"Dat Thanh Pham, Khai Quang Tran, Viet Anh Nguyen","doi":"10.1186/s13040-025-00492-3","DOIUrl":"10.1186/s13040-025-00492-3","url":null,"abstract":"<p><strong>Background and objective: </strong>Estimating individualized causal effects plays a vital role in data-driven decision-making, especially in high-risk domains such as public health. However, current causal inference models often lack flexibility and generalizability due to the tight coupling between representation learning and effect estimation. This study aims to develop a modular and adaptive framework to enhance the analysis of individualized causal effects in complex health data.</p><p><strong>Methods: </strong>We propose CAUSALRLSTACK, a modular framework designed to separate representation learning from causal effect estimation. In practice, the model uses a memory-augmented Transformer (TITAN) to capture complex, individualized representations. It is further paired with a doubly robust estimator(DRLearner) to improve the treatment effect estimation. A reinforcement learning agent adjusts how much each component contributes by assigning instance-specific weights. This adaptive weighting process improves the model's ability to generalize across different populations. Input features are derived from causal graphs, automatically chosen between an expert-defined graph and one discovered from data. To evaluate performance, we applied the framework to two publicly available HIV datasets that reflect community-level testing behavior and post-intervention clinical outcomes.</p><p><strong>Results: </strong>CAUSALRLSTACK outperforms six state-of-the-art causal inference models across both datasets, achieving the highest accuracy (0.861 and 0.855), F1-Score (0.845 and 0.839), and AUC-ROC (0.897 and 0.892). It also achieves the lowest predictive uncertainty (0.093 and 0.092), indicating robust performance in estimating treatment effects.</p><p><strong>Conclusions: </strong>The proposed framework offers a flexible and effective solution for individualized causal inference. Its modular architecture and reinforcement learning-based weighting strategy enable adaptive, data-driven estimation across diverse populations. Strong experimental results demonstrate the potential of the framework to advance individualized causal inference in health data and provide a practical basis for designing personalized intervention strategies in HIV and broader public health domains.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"77"},"PeriodicalIF":6.1,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12587697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145453252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-driven TCRβ repertoire analysis enhances diagnosis and enables mining of immunological biomarkers in systemic lupus erythematosus.","authors":"Tongfei Shen, Yifei Sheng, Wan Nie, Shuo Yang, Kaiqi Li, Ziwei Ma, Zhao Ling, Bowen Tan, Xikang Feng, Miaozhe Huo","doi":"10.1186/s13040-025-00490-5","DOIUrl":"10.1186/s13040-025-00490-5","url":null,"abstract":"<p><strong>Background: </strong>Systemic Lupus Erythematosus (SLE) is a complex autoimmune disorder involving dysregulation of multiple immune components, including T cells. Aberrant T-cell activity contributes significantly to the immune pathology of SLE, for instance, by facilitating autoantibody production. The Complementarity Determining Region 3 (CDR3) of the TCRβ chain is pivotal for T-cell specificity, thereby positioning it as a promising target for enhancing diagnostic accuracy and gaining deeper mechanistic insights into SLE. To address these diagnostic limitations in SLE, our team developed DeepTAPE, a deep learning-based diagnostic framework that utilizes CDR3 sequences to achieve robust classification performance for SLE.</p><p><strong>Results: </strong>Building upon the foundation established by DeepTAPE, we devised a novel diagnostic approach that effectively integrates a TCR classifier to quantify SLE disease activity. Furthermore, this methodology employs advanced deep learning models for the bio-mining of disease-associated motifs that serve as potential biomarkers. As a result, this approach generates an autoimmune risk score (ARS) indicative of SLE probability. Notably, this ARS metric exhibited a strong correlation with disease activity, functioning as a quantitative clinical marker that complements traditional indices such as the SLE Disease Activity Index (SLEDAI). In addition, through a comprehensive analysis of immune repertoire data, we identified SLE-specific amino acid motifs within the CDR3 sequences, including critical 3-mer and gapped-mer oligopeptides. These motifs demonstrated high efficacy in SLE classification, achieving an area under the curve (AUC) of 0.908, thereby significantly outperforming other candidate biomarkers. Moreover, our model revealed potential SLE-associated antigens and genes, such as CD109 and INS, which provide new insights into the immunological mechanisms underlying the disease.</p><p><strong>Conclusion: </strong>This study highlights the potential of DeepTAPE as a supportive tool for biomarker discovery and assessing SLE disease activity, which complements traditional diagnostic approaches. By deepening our understanding of the immunological characteristics and mechanisms associated with SLE, this work lays a foundation for advancing targeted therapies and personalized medicine in autoimmune diseases. Consequently, our findings may pave the way for improved patient outcomes and more effective treatment strategies in the management of SLE.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"76"},"PeriodicalIF":7.9,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12577242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145423186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}