Interdisciplinary Sciences: Computational Life Sciences最新文献

{"title":"iEnhancer-Flow: Integrating Transformer-Based Sequence Learning with DNA Shape Insights for Robust Enhancer Prediction.","authors":"Huan Liu, Hanyu Luo, Lingyun Luo, Pingjian Ding","doi":"10.1007/s12539-025-00787-3","DOIUrl":"10.1007/s12539-025-00787-3","url":null,"abstract":"<p><strong>Purpose: </strong>Enhancers are critical non-coding regulatory elements, but their prediction remains challenging due to their variability and the absence of clear sequence motifs. This study aims to promote enhancer classification through a novel framework integrating DNA sequence and shape features, addressing the limitations of sequence-only models and improving prediction performance across diverse genomic contexts.</p><p><strong>Methods: </strong>We propose iEnhancer-Flow, a dual-branch model that integrates DNABERT-2 for extracting robust sequence representations and a hybrid convolutional network-based branch for DNA shape information. Drawing inspiration from central-difference techniques in image processing, the shape branch utilizes similar methods to capture local structural variations. The extracted sequence and shape features are fused via a flow attention mechanism to facilitate dynamic interaction between these complementary feature sets. The combined features are further enhanced with a weighted residual connection and attention pooling before being passed to an MLP classifier for final enhancer prediction.</p><p><strong>Results: </strong>iEnhancer-Flow consistently outperformed competing methods, achieving significant improvements in balanced accuracy (Bacc), Matthews correlation coefficient (MCC), and other key metrics across six of the eight cell lines tested. For the remaining two cell lines, the model achieved comparable performance across several key metrics, suggesting its stability and robustness in diverse biological contexts.</p><p><strong>Conclusion: </strong>The integration of sequence and DNA shape information in iEnhancer-Flow marks a significant advancement in enhancer prediction by capturing complementary regulatory signals beyond traditional sequence features. These findings suggest that understanding genomic regulation requires a comprehensive view, incorporating both sequence and structural contexts.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"894-911"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145587294","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}

{"title":"A Hypergraph-Based Model for Predicting Potential Drug Combinations in Cancer Therapy.","authors":"Qi Wang, Zhiheng Zhou, Guiying Yan","doi":"10.1007/s12539-025-00779-3","DOIUrl":"10.1007/s12539-025-00779-3","url":null,"abstract":"<p><p>Finding effective drug combinations is a pivotal strategy for enhancing therapeutic efficacy and overcoming drug resistance in complex diseases like cancer. While computational methods have accelerated this discovery, most existing models are confined to predicting pairwise interactions, failing to capture the complex, higher-order synergies inherent in multi-drug regimens. To bridge this critical gap, we introduce an enhanced hypergraph random walk (EHRW) model uniquely designed to predict effective drug combinations. Our framework naturally represents multi-drug relationships using hypergraphs and leverages network topology to predict combination efficacy. Recognizing that network structure alone may not fully capture the intricate biological properties of drugs, we further propose a robust post-processing strategy that refines initial predictions by integrating auxiliary drug features. This method, which uses chemical similarity derived from SMILES fingerprints, serves as a powerful validation layer, significantly boosting the model's predictive accuracy. We demonstrate the superior performance of our enhanced EHRW model through rigorous validation on two major cancer datasets (lung and breast cancer). Our results show that the chemical similarity-based post-processing strategy outperforms the original model and several contemporary baselines. Importantly, our model extends beyond binary prediction by introducing a straightforward scoring method for three-drug combinations, which averages the predicted scores of their constituent binary pairs and provides a practical pathway for evaluating higher-order therapies. The enhanced EHRW model offers a flexible, accurate, and scalable computational tool, paving the way for more precise discovery of effective multi-drug regimens.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"838-848"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145336969","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}

{"title":"GN-Net: A Geometric and Neighborhood-Aware Network for Predicting Intracranial Aneurysm Rupture Risk and Assisting Clinical Decision-Making.","authors":"Jiafeng Zhou, Peiying Li, Yongchang Liu, Shikui Tu, Bing Zhao, Jieqing Wan, Yongchun Chen, Yunjun Yang, Lei Xu","doi":"10.1007/s12539-025-00785-5","DOIUrl":"10.1007/s12539-025-00785-5","url":null,"abstract":"","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"879-893"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145660921","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}

{"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":"10.1007/s12539-025-00768-6","url":null,"abstract":"<p><p>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 .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1080-1098"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","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}

{"title":"MDL-HTI: A Multimodal Deep Learning Approach for Predicting Herb-Target Interactions.","authors":"Lianzhong Zhang, Xiumin Shi, Xiaohong Deng","doi":"10.1007/s12539-025-00772-w","DOIUrl":"10.1007/s12539-025-00772-w","url":null,"abstract":"<p><strong>Purpose: </strong>Traditional Chinese medicine (TCM) has garnered increasing attention from the global medical community due to its unique therapeutic principles and extensive medicinal resources. Understanding herb-target interactions (HTIs) is crucial for elucidating the pharmacological mechanisms that link herbal medicines to biological targets, offering valuable insights into the precise effects of herbal therapeutics. However, current methods exhibit limited effectiveness and fail to fully leverage the biological information associated with herbs and targets.</p><p><strong>Methods: </strong>We propose MDL-HTI, a novel framework that integrates heterogeneous graph learning with multimodal biological data. The architecture employs a heterogeneous graph learning network based on the multi-view heterogeneous relation embedding (MV-HRE) algorithm to extract structural patterns from subgraphs, meta-paths, and communities, alongside a biological multimodal information network that encodes herbal ingredients, target pathways, and ligand properties into unified vectors. A relational prediction network with self-attention dynamically fuses features from both components to identify potential HTIs.</p><p><strong>Results: </strong>MDL-HTI demonstrates superior performance compared to state-of-the-art baselines. Furthermore, case study validation confirms that our model can serve as an effective tool for identifying potential HTIs.</p><p><strong>Conclusion: </strong>This work establishes a novel computational paradigm for TCM pharmacology by integrating topological learning with multimodal biological encoding. MDL-HTI provides a robust platform for elucidating TCM mechanisms and accelerating the discovery of multi-target herbs. The framework has potential applications in precision and personalized medicine, and its predictive capability may significantly reduce experimental costs while improving therapeutic outcomes for complex conditions.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"774-788"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145377151","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}

{"title":"DSGCNLDA: A Multi-view Learning Model with DualScope Attention for lncRNA-Disease Association Prediction.","authors":"Dengju Yao, Zhanhe Li, Xiaojuan Zhan, Bo Zhang, Xiangkui Li","doi":"10.1007/s12539-025-00790-8","DOIUrl":"10.1007/s12539-025-00790-8","url":null,"abstract":"<p><p>Long non-coding RNAs (lncRNAs) play key regulatory roles in biological activities, making it crucial to accurately predict lncRNA-disease relationships for understanding disease pathophysiology and developing effective prevention and treatment strategies. Nevertheless, existing computational prediction methods are often limited by data sparsity and incompleteness, as well as the inadequate representation of node information. To mitigate these issues, a novel prediction model, termed DSGCNLDA, is proposed to enhance predictive performance. The proposed model integrates biological similarity features from multiple perspectives into a comprehensive similarity matrix using multi-view fusion learning. Utilizing this similarity matrix in conjunction with the adjacency matrix, a heterogeneous network of lncRNA-disease associations is constructed. Subsequently, feature extraction is conducted on the randomly masked heterogeneous network. During this process, a graph convolutional network (GCN) is employed as the encoder, and our proposed DualScope attention mechanism is introduced to more effectively capture complex topological relationships between nodes, thereby obtaining a comprehensive representation of the nodes. Finally, association prediction is made via a multi-layer perceptron (MLP). Experimental results on multiple public datasets show that DSGCNLDA performs strongly in lncRNA-disease association prediction. Ablation studies confirm its novelty, while case studies and generalization evaluations demonstrate its effectiveness in biomedical prediction tasks.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"947-966"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145633138","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}

{"title":"IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction.","authors":"Xiaoyang Li, Yuhao Zhang, Yafei Liu, Xinyu Lu, Peirong Ma, Yafei Li, Masaru Kitsuregawa, Yanhui Gu","doi":"10.1007/s12539-025-00781-9","DOIUrl":"10.1007/s12539-025-00781-9","url":null,"abstract":"<p><p>Drug-drug interactions (DDIs) are crucial throughout various stages of drug development. Using computer-aided methods for accurate prediction of DDIs can enhance clinical safety and accelerate drug discovery. However, most existing deep learning methods heavily rely on the connectivity information between drugs. The neglect of the large number of potential DDI relationships can hinder the model's ability to extract meaningful information, thereby limiting its generalization capacity. To address these limitations, we propose IMF-DDI, an innovative DDI prediction framework that obtains drug molecule representations for DDI prediction by combining information from multiple external entities. First, our proposed information mapping module enables the model to capture the associations between drug molecules in terms of their interactions with multiple external entities. Meanwhile, the multi-source information fusion module efficiently integrates information from multiple external entities to generate the final representations of drug molecules. We carefully designed three distinct experimental tasks to validate the effectiveness of IMF-DDI. Our method establishes the current state-of-the-art across all tasks on the DrugBank dataset, while achieving the best performance in most tasks on the TWOSIDES dataset.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"849-865"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145563979","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}

{"title":"Subgraph Neural Networks Enhanced by Global Similarity for Drug Repositioning.","authors":"Chengyan Zhou, Xinliang Sun, Xiang Du, Min Zeng, Min Li","doi":"10.1007/s12539-025-00747-x","DOIUrl":"10.1007/s12539-025-00747-x","url":null,"abstract":"<p><p>Drug repositioning is a promising strategy for accelerating drug development and reducing costs by identifying potential indications for existing drugs. Recently, technological advancements have enabled the development of numerous graph convolutional network (GCN)-based methods for drug repositioning. However, many existing methods overlook the distinct roles of nodes within drug-disease association graphs, limiting their ability to learn effective representations. To address this limitation, we propose a subgraph neural network enhanced by global similarity for drug repositioning, termed GSESNN. Specifically, GSESNN first extracts the subgraph of each drug-disease pair from the entire drug-disease graph. Then, GCN and a sort pooling strategy are utilized to learn the subgraph representation. In addition, to distinguish between different drug-disease pairs with the identical subgraph topology, GSESNN utilizes GCN to learn the similarity information of drugs and diseases, fusing it with the subgraph representation to produce the final representation. Finally, we regard the drug-disease association prediction as a graph classification task. Experimental results show that GSESNN outperforms the baseline model in drug repositioning tasks. Case studies on Alzheimer's disease and Gastric Cancer further demonstrate that our model successfully identifies more accurate drug-disease associations, highlighting its potential for practical applications in drug discovery.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"763-773"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13469388/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145400687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurately Predicting Cell Type Abundance from Spatial Histology Image Through HPCell.","authors":"Yongkang Zhao, Youyang Li, Weijiang Yu, Hongyu Zhang, Zheng Wang, Yuedong Yang, Yuansong Zeng","doi":"10.1007/s12539-025-00757-9","DOIUrl":"10.1007/s12539-025-00757-9","url":null,"abstract":"<p><p>Recent advancements in spatial transcriptomics (ST) have revolutionized our ability to simultaneously profile gene expression, spatial location, and tissue morphology, enabling the precise mapping of cell types and signaling pathways within their native tissue context. However, the high cost of sequencing remains a significant barrier to its widespread adoption. Although existing methods often leverage histopathological images to predict transcriptomic profiles and identify cellular heterogeneity, few approaches directly estimate cell-type abundance from these images. To address this gap, we propose HPCell, a deep learning framework for inferring cell-type abundance directly from H&E-stained histology images. HPCell comprises three key modules: a pathology foundation module, a hypergraph module, and a Transformer module. It begins by dividing whole-slide images (WSIs) into patches, which are processed by the pathology foundation module using a teacher-student framework to extract robust morphological features. These features are used to construct a hypergraph, where each patch (node) connects to its spatial neighbors to model complex many-to-many relationships. The Transformer module applies attention to the hypergraph features to capture long-range dependencies. Finally, features from all modules are integrated to estimate cell-type abundance. Extensive experiments show that HPCell consistently outperforms state-of-the-art methods across multiple spatial transcriptomics datasets, offering a scalable and cost-effective approach for investigating tissue structure and cellular interactions.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1099-1112"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992470","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}

{"title":"Unveiling the Impact of Copper Metabolism on Epithelial-Mesenchymal Transition of Triple-Negative Breast Cancer: Identification of Therapeutic Targets.","authors":"Kai Zhuang, Lin Yan, Muhammad Waqas, Haixin Feng, Siqi Tang, Haoqi He, Xinshi Yu, Yanling Wang, Zunnan Huang","doi":"10.1007/s12539-026-00835-6","DOIUrl":"10.1007/s12539-026-00835-6","url":null,"abstract":"<p><p>Triple-negative breast cancer (TNBC) is a biologically aggressive subtype of breast cancer marked by high heterogeneity and poor prognosis. Copper metabolism has been implicated in TNBC progression, but its functional contributions remain insufficiently defined. In this study, we analyzed transcriptomic data from 229 TNBC and adjacent normal samples from The Cancer Genome Atlas (TCGA) to identify 26 differentially expressed copper metabolism-related genes (DEGs_-CM). A nine-gene Cox model (HEPHL1, COX7A1, COX4I2, JUN, MAPT, MT1A, AOC3, DCT, AOC2) demonstrated robust prognostic value, with time-dependent AUCs of 0.88, 0.84, and 0.80 at 1, 3, and 5 years. Functional enrichment analyses revealed epithelial-mesenchymal transition (EMT) and angiogenesis pathways enriched in high-risk groups. Four genes (AOC3, COX4I2, COX7A1, JUN) were further identified as copper metabolism-related metastasis genes (CMMRGs) through correlation with metastasis-associated programs. Based on these genes, machine learning classifiers were developed to predict TNBC presence and lymph node metastasis. Classifiers trained on the full dataset achieved consistently high performance, with most models showing AUCs greater than 0.97 (random forest, XGBoost, and AdaBoost classifier) even when using reduced gene panels (26-, 9-, and 4-gene sets), demonstrating stable classification across gene panels. In contrast, performance declined notably in metastasis-specific classification, largely due to the limited number of labeled metastatic samples. Among these, the 9-gene panel yielded the highest test AUCs across most models (gradient boosting machine AUC 0.64), suggesting that it may provide an optimal balance between model complexity and discriminative power, while also highlighting a key limitation related to the restricted sample size and incomplete clinical annotations in the metastasis-specific dataset. Single-cell RNA sequencing confirmed fibroblast-specific enrichment of CMMRGs and associated EMT signatures, suggesting a mechanistic link between copper metabolism, stromal remodeling, and metastasis. These results establish a copper-centered molecular framework for TNBC diagnosis and metastasis prediction, supporting the translational potential of copper metabolism-related genes in clinical applications.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1145-1164"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148137932","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}