Interdisciplinary Sciences: Computational Life Sciences最新文献

TrambaHLApan: A Transformer and Mamba-based Neoantigen Prediction Method Considering both Antigen Presentation and Immunogenicity. TrambaHLApan：一种考虑抗原呈递和免疫原性的基于变压器和曼巴的新抗原预测方法。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-10-14 DOI: 10.1007/s12539-025-00777-5

Yibo Zhu, Xiumin Shi, Lu Wang, Jingjuan Zhang

{"title":"TrambaHLApan: A Transformer and Mamba-based Neoantigen Prediction Method Considering both Antigen Presentation and Immunogenicity.","authors":"Yibo Zhu, Xiumin Shi, Lu Wang, Jingjuan Zhang","doi":"10.1007/s12539-025-00777-5","DOIUrl":"https://doi.org/10.1007/s12539-025-00777-5","url":null,"abstract":"Neoantigens, tumor-specific peptides with immunogenic potential, represent pivotal targets for cancer immunotherapy. Existing methods prioritize HLA-peptide binding but often fail to adequately address immunogenicity, limiting their clinical utility. This study introduces TrambaHLApan, a novel neoantigen prediction framework that integrates Transformer and Mamba architectures to concurrently predict antigen presentation likelihood (TrambaHLApan-EL) and immunogenic potential (TrambaHLApan-IM). A Transformer-based encoding module is employed to generate unique representations for HLA molecules and peptides. Subsequently, a hybrid fusion module, which combines merged-attention mechanisms with Mamba-based sequential modeling, is deployed to evaluate interaction patterns. TrambaHLApan-IM incorporates antigen presentation scores derived from TrambaHLApan-EL to explicitly model the interplay between antigen presentation and immunogenic potential, thereby enhancing the identification of neoantigens with high confidence. Experimental results on independent datasets demonstrate that TrambaHLApan outperforms state-of-the-art methods, establishing it as a reliable tool for advancing personalized cancer immunotherapies.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145292048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting miRNA-Drug Interactions Based on Multi-source Feature Fusion of Heterogeneous Network. 基于异构网络多源特征融合的mirna -药物相互作用预测。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-10-13 DOI: 10.1007/s12539-025-00775-7

Chenyue Lei, Xiujuan Lei, Lian Liu, Jianrui Chen, Fang-Xiang Wu

{"title":"Predicting miRNA-Drug Interactions Based on Multi-source Feature Fusion of Heterogeneous Network.","authors":"Chenyue Lei, Xiujuan Lei, Lian Liu, Jianrui Chen, Fang-Xiang Wu","doi":"10.1007/s12539-025-00775-7","DOIUrl":"https://doi.org/10.1007/s12539-025-00775-7","url":null,"abstract":"Resistance to treatment remains one of the greatest challenges in cancer therapy. Recent studies have shown that drug sensitivity is closely associated with miRNA expression, highlighting the importance of predicting miRNA-drug interactions (MDIs) in understanding drug resistance mechanisms. Within this study, we propose an innovative method named MSFFMDI, which employs a dual-channel multi-source feature fusion framework based on heterogeneous networks to predict potential MDIs. The first channel focuses on attribute feature extraction. For miRNAs, we integrate the k-mer algorithm with word2vec to transform sequences into low-dimensional embeddings that capture semantic and structural information. For drugs, we utilize the graph isomorphism network to learn molecular structure features, and apply mol2vec to capture chemical and functional sequence features. The second channel extracts topological features by constructing a heterogeneous network based on integrated similarities and known associations between miRNAs and drugs. A graph attention network is used to update node embeddings, and a multi-scale convolutional neural network is employed to further extract topological representations. The features from both channels are fused and reduced via principal component analysis before being used for final prediction. A large number of rich experimental results show that MSFFMDI demonstrates excellent predictive performance on two datasets. Case studies further validate its robust performance. Overall, MSFFMDI provides a powerful and interpretable framework for predicting MDIs and offers potential insights into the mechanisms of drug resistance.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MPMB-DR: Meta-path Integration of Multi-source Biological Information for Drug Repositioning. MPMB-DR：用于药物重新定位的多源生物信息元路径集成。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-10-13 DOI: 10.1007/s12539-025-00771-x

Xiaoyan Sun, Zhenjie Hou, Wenguang Zhang, Yan Chen, Haibin Yao

{"title":"MPMB-DR: Meta-path Integration of Multi-source Biological Information for Drug Repositioning.","authors":"Xiaoyan Sun, Zhenjie Hou, Wenguang Zhang, Yan Chen, Haibin Yao","doi":"10.1007/s12539-025-00771-x","DOIUrl":"https://doi.org/10.1007/s12539-025-00771-x","url":null,"abstract":"Conventional approaches to drug discovery often require considerable time and effort. The promising solution is to repurpose existing drugs by identifying new therapeutic roles, thereby enhancing development efficiency. Drug repositioning based on computational methods is gaining widespread attention. However, most computational methods primarily rely on similarity-based data to extract features of associations, but lack the mining of topological structural features in the association network, while ignoring valuable original biological and chemical information. Therefore, this article develops a drug repositioning approach via meta-path integration of multi-source biological information (MPMB-DR). This approach combines meta-path and biomolecular similarity information to construct high-quality negative links within heterogeneous networks. It considers both the topological structure of the association network and the relationships among biomolecules. Based on the negative sample strategy, potential drug-disease associations are predicted by leveraging the synergy between meta-paths and multi-source biological data. Experimental results and case studies demonstrate that the MPMB-DR method has significant advantages in identifying associations between potential drugs and diseases.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Potential Microbe-Disease Associations Based on Heterogeneous Graph Random Attention Neural Network and Neural Collaborative Filtering. 基于异构图随机注意神经网络和神经协同过滤的潜在微生物与疾病关联预测。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-10-13 DOI: 10.1007/s12539-025-00776-6

Bo Wang, Wenlong Zhao, Xiaoxin Du, Jianfei Zhang, Jingyou Li, Hang Sun

{"title":"Predicting Potential Microbe-Disease Associations Based on Heterogeneous Graph Random Attention Neural Network and Neural Collaborative Filtering.","authors":"Bo Wang, Wenlong Zhao, Xiaoxin Du, Jianfei Zhang, Jingyou Li, Hang Sun","doi":"10.1007/s12539-025-00776-6","DOIUrl":"https://doi.org/10.1007/s12539-025-00776-6","url":null,"abstract":"Extensive research has underscored the intricate relationships between microbial communities and human diseases. Delving into these associations enhances our understanding of disease mechanisms and facilitates the development of novel therapeutic strategies. Although traditional biological methods for identifying microbe-disease association (MDA) are reliable, they often entail high costs, extended timelines, and substantial manual effort. To address these limitations, this study introduces GRNCFMDA, an advanced deep learning framework designed to improve MDA prediction efficiency. Initially, the model integrates functional and Gaussian interaction profile (GIP) similarities of microbes, along with semantic and GIP similarities of diseases, to construct a comprehensive heterogeneous network. A graph random neural network (GRAND) enhanced with attention mechanisms is then applied to derive informative high-order representations of microbe and disease nodes. This is followed by a neural collaborative filtering module that merges the strengths of generalized matrix factorization for linear modeling with the deep learning capacity of multilayer perceptrons for capturing nonlinear patterns. Performance evaluations based on five-fold cross-validation across HMDAD and Disbiome datasets show that GRNCFMDA consistently outperforms four existing MDA prediction models. Additionally, empirical case studies affirm the model's practical utility in uncovering novel MDA. The implementation and datasets are publicly available at https://github.com/chenyunmolu/GRNCFMDA .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising. 基于拓扑结构保存和数据去噪的单细胞RNA-seq数据可解释多任务分析。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-09-29 DOI: 10.1007/s12539-025-00765-9

Shengpeng Yu, Zihan Yang, Tianyu Liu, Cheng Liang, Hong Wang

{"title":"Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising.","authors":"Shengpeng Yu, Zihan Yang, Tianyu Liu, Cheng Liang, Hong Wang","doi":"10.1007/s12539-025-00765-9","DOIUrl":"https://doi.org/10.1007/s12539-025-00765-9","url":null,"abstract":"The advent of single-cell transcriptome sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the individual cell level, overcoming the limitations of bulk RNA sequencing. However, the explosive growth of scRNA-seq data and the prevalence of dropout events pose significant challenges for downstream analysis. Existing methodologies often focus on isolated tasks, such as identifying cell communities, processing dropout events, and mitigating batch effects, neglecting collaborative multi-task analysis, and introducing new noise during dropout event handling. In response to these challenges, we propose scIMTA (interpretable multi-task analysis of single-cell), an advanced framework designed to enhance interpretability and effectively address the issues of topological structure preservation and dropout events. The key innovations of scIMTA are that scIMTA enables collaborative multi-task analysis of sparse, high-noise gene expression data, enhances interpretability through biological grounding, robustly handles dropout events by preserving data integrity, and demonstrates efficacy and generalizability through rigorous validation on breast cancer scRNA-seq datasets. scIMTA establishes a new framework for collaborative multi-task analysis, interpretability, and robust dropout handling in single-cell transcriptome studies. This work significantly advances the field and allows a more nuanced exploration of cellular heterogeneity and gene expression dynamics. The source code of scIMTA is available for download at https://github.com/ShengPengYu/scIMTA .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable Cancer Survival Prediction by Fusing Semantic Labelling of Cell Types and Whole Slide Images. 融合细胞类型和整个幻灯片图像语义标记的可解释癌症生存预测。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-09-26 DOI: 10.1007/s12539-025-00744-0

Jinchao Chen, Pei Liu, Chen Chen, Ying Su, Jiajia Wang, Cheng Chen, Xiantao Ai, Xiaoyi Lv

{"title":"Interpretable Cancer Survival Prediction by Fusing Semantic Labelling of Cell Types and Whole Slide Images.","authors":"Jinchao Chen, Pei Liu, Chen Chen, Ying Su, Jiajia Wang, Cheng Chen, Xiantao Ai, Xiaoyi Lv","doi":"10.1007/s12539-025-00744-0","DOIUrl":"https://doi.org/10.1007/s12539-025-00744-0","url":null,"abstract":"Survival prediction involves multiple factors, such as histopathological image data and omics data, making it a typical multimodal task. In this work, we introduce semantic annotations for genes in different cell types based on cell biology knowledge, enabling the model to achieve interpretability at the cellular level. Since these cell type annotations are derived from the unique sites of origin for each cancer type, they can be more closely aligned with morphological features in whole slide images (WSIs) and address the issue of genomic annotation ambiguity. We then propose a multimodal fusion model, SurvTransformer, with multi-layer attention to fuse cell type tags (CTTs) and WSIs for survival prediction. Finally, through attention and integrated gradient attribution, the model provides biologically meaningful interpretable analysis at three different levels: cell type, gene, and histopathology image. Comparative experiments show that SurvTransformer achieves the highest consistency index across four cancer datasets. The survival curves generated are also statistically significant. Ablation experiments show that SurvTransformer outperforms models based on different labeling methods and attention representations. In terms of interpretability, case studies validate the effectiveness of SurvTransformer at three levels: cell type, gene, and histopathological image.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

hERG-MFFGNN: An Explainable Deep Learning Model for Predicting Cardiotoxicity Using Multi-feature Fusion and Graph Neural Networks. heg - mffgnn：一种可解释的深度学习模型，用于使用多特征融合和图神经网络预测心脏毒性。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-09-22 DOI: 10.1007/s12539-025-00768-6

Bingyu Jin, Jiarun Wang, Xin Yang, Lijie Na, Qi Zhao

{"title":"hERG-MFFGNN: An Explainable Deep Learning Model for Predicting Cardiotoxicity Using Multi-feature Fusion and Graph Neural Networks.","authors":"Bingyu Jin, Jiarun Wang, Xin Yang, Lijie Na, Qi Zhao","doi":"10.1007/s12539-025-00768-6","DOIUrl":"https://doi.org/10.1007/s12539-025-00768-6","url":null,"abstract":"Drug-related cardiotoxicity, most notably arrhythmia, represents a major challenge in drug development. Inhibition of hERG potassium channel by certain compounds has the potential to delay cardiac repolarization, manifested as QT interval prolongation, thereby elevating the risk of severe cardiac arrhythmias like torsades de pointes (TdP). Accurate assessment of compounds' impact on hERG channels is crucial. Traditional methods are costly and inefficient for large-scale screening. Therefore, developing efficient and accurate computational methods for hERG inhibition prediction is critical. In this study, we present a deep learning framework, named hERG-MFFGNN, aimed at accurately predicting hERG channel blockers while providing model interpretability. To improve both accuracy and generalizability, we implement a multi-feature fusion strategy that systematically integrates molecular structural information. Initially, multiple molecular fingerprint features and molecular descriptors are fused to construct an initial feature representation. Then, graph neural networks are used to extract molecular topological features. These two sets of features are weighted and fused using an attention mechanism to form the final compound representation, enabling a more comprehensive expression of molecular features. The performance of hERG-MFFGNN is assessed using fivefold cross-validation on the benchmark dataset and external validation datasets. The results demonstrate that hERG-MFFGNN achieves AUROC of 0.909 and ACC of 0.854, highlighting its robust predictive capabilities for hERG activity across diverse datasets. We believe that may function as an effective instrument for the early prediction of hERG channel blockers in the phases of drug discovery and development. The complete source code is publicly accessible at https://github.com/zhaoqi106/hERG-MFFGNN .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics. MKLNID：通过多核学习和网络脉冲动力学识别黑色素瘤相关致病基因。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-09-22 DOI: 10.1007/s12539-025-00755-x

Linconghua Wang, Ju Xiang, Zihao Guo, Kaixin Zeng, Min Li

{"title":"MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics.","authors":"Linconghua Wang, Ju Xiang, Zihao Guo, Kaixin Zeng, Min Li","doi":"10.1007/s12539-025-00755-x","DOIUrl":"https://doi.org/10.1007/s12539-025-00755-x","url":null,"abstract":"Melanoma is a highly malignant skin cancer, and identifying its pathogenic genes is crucial for understanding its pathogenesis and developing treatment strategies. Network-based approaches effectively capture the synergistic interactions among genes and their products within biological systems, yet extracting functional insights from these complex networks remains challenging. Here, we propose a novel approach that combines multiple kernel learning and network impulsive dynamics (MKLNID) to predict melanoma-related pathogenic genes. Specifically, we construct similarity kernels of diseases and genes from the original disease-gene heterogeneous network and melanoma expression profiles. These kernels are integrated via multiple kernel learning to generate enhanced similarity networks for diseases and genes, respectively. Impulsive signals are then applied to specific nodes in the enhanced heterogeneous network, and the resulting dynamical response signatures are used to infer potential pathogenic genes. Comprehensive experiments and case analyses demonstrate the effectiveness of MKLNID in identifying melanoma-related genes. By deeply integrating heterogeneous disease networks with omics data and introducing network dynamics to simulate gene responses, MKLNID offers a new strategy for identifying melanoma-related genes, with potential implications for precision diagnosis and therapy.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal Transformer for Learning Embeddings from Structured Medical History Records and Multi-Source Data Integration for Complex Disease Risk Prediction. 从结构化病历记录中学习嵌入的因果转换器和用于复杂疾病风险预测的多源数据集成。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-09-17 DOI: 10.1007/s12539-025-00749-9

Zeming Li, Yu Xu, Debajyoti Chowdhury, Hip Fung Yip, Chonghao Wang, Lu Zhang

{"title":"Causal Transformer for Learning Embeddings from Structured Medical History Records and Multi-Source Data Integration for Complex Disease Risk Prediction.","authors":"Zeming Li, Yu Xu, Debajyoti Chowdhury, Hip Fung Yip, Chonghao Wang, Lu Zhang","doi":"10.1007/s12539-025-00749-9","DOIUrl":"https://doi.org/10.1007/s12539-025-00749-9","url":null,"abstract":"Traditional disease risk prediction models predominantly rely on statistical algorithms and often focus on genetic factors or a limited set of lifestyle factors to estimate the risk of disease onset. Recently, more comprehensive approaches have emerged that integrate genetic factors with additional lifestyle factors (e.g., alcohol intake) and physical features (e.g., body mass index, age) to increase predictive accuracy. Since the onset of complex diseases is often accompanied by the occurrence of comorbidities, incorporating medical history records is a critical yet underexplored avenue for improving risk prediction. In this study, we propose a novel framework, MIDRP (Multi-source Integration for Disease Risk Prediction), which incorporates genetic variants, lifestyle factors, physical attributes, and medical history records to achieve more robust and accurate predictions. At the heart of our approach lies a causal Transformer architecture, specifically designed to extract and interpret nuanced patterns from medical history records. In the experiments, we compared MIDRP with several baselines, including LDPred2, random forest, multilayer perception, logistic regression, AdaBoost, DiseaseCapsule, EIR, and Med-Bert, on three complex diseases Coronary Artery Disease, Type 2 Diabetes, and Breast Cancer using data from the UK Biobank. Our method achieved state-of-the-art performance, AUROC scores of 0.783, 0.841, and 0.784, respectively, demonstrating its potential in the field of complex disease risk prediction.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145080571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ORMCKB: A Knowledge Database for Personalized Medicine in Deciphering the Oral Microbiome-Disease Axis. ORMCKB：一个个性化医疗知识库，用于破译口腔微生物群-疾病轴。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-09-17 DOI: 10.1007/s12539-025-00769-5

Yutao Wu, Yi Zhou, Wenjing Shi, Siyu Zhou, Min Jiang, Ke Shen, Xingyun Liu, Xiaoyu Li, Jiao Wang, Chi Zhang, Bairong Shen, Weidong Tian

{"title":"ORMCKB: A Knowledge Database for Personalized Medicine in Deciphering the Oral Microbiome-Disease Axis.","authors":"Yutao Wu, Yi Zhou, Wenjing Shi, Siyu Zhou, Min Jiang, Ke Shen, Xingyun Liu, Xiaoyu Li, Jiao Wang, Chi Zhang, Bairong Shen, Weidong Tian","doi":"10.1007/s12539-025-00769-5","DOIUrl":"https://doi.org/10.1007/s12539-025-00769-5","url":null,"abstract":"The oral microbiome plays a crucial role in the development and progression of diseases. The complex interactions between the oral microbiome and diseases are challenging for clinicians in clinical decision-making and scientific research. To address this gap, we developed an oral microbiome knowledge database (ORMCKB), to provide evidence for personalized medicine and scientific research in the oral microbiome-disease axis. The current version of ORMCKB contains 11,554 data entries, encompassing 6941 oral microbe taxonomies, 234 diseases, 220 interventions, and 175 bacteriostats extracted from 818 publications. Compared to ChatGPT-4o, ORMCKB demonstrates superior performance in matching questions with responses (10 vs. 9.6), presenting research article details (10 vs. 5.80), and recommended scientific article authenticity ratio (100% vs. 33.63%). The system usability scale (SUS) and the net promoter score (NPS) were 86.07 and 85.71, respectively. As the first knowledge database focused on the oral microbiome-disease axis, ORMCKB provides a comprehensive, accurate, and user-friendly online resource for identifying key microbial players and their associations with oral diseases in personalized medicine. ORMCKB is set to sustain its prominence in cutting-edge research on the oral microbiome-disease axis, paving the way for future artificial intelligence applications in both scientific research and clinical practice. ORMCKB is publicly available at: http://sysbio.org.cn/ormckb.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145080607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0