Interdisciplinary Sciences: Computational Life Sciences最新文献_第2页

Accurate RNA-Ligand Binding Site Prediction Based on a Multi-Channel Graph Neural Network. 基于多通道图神经网络的rna -配体结合位点精确预测。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-19 DOI: 10.1007/s12539-026-00883-y

Na Li, Jingran Niu, Zhendong Liu, Jiamin Jiang, Bingbing Guo, Yujie Li, Jiafeng Yu, Dongqing Wei, Rongjun Man

{"title":"Accurate RNA-Ligand Binding Site Prediction Based on a Multi-Channel Graph Neural Network.","authors":"Na Li, Jingran Niu, Zhendong Liu, Jiamin Jiang, Bingbing Guo, Yujie Li, Jiafeng Yu, Dongqing Wei, Rongjun Man","doi":"10.1007/s12539-026-00883-y","DOIUrl":"https://doi.org/10.1007/s12539-026-00883-y","url":null,"abstract":"RNA-ligand binding-site prediction is a challenging task in RNA molecular analysis. Binding regions are often sparse, structurally heterogeneous, and difficult to delineate accurately at the nucleotide level. Existing sequence-based methods lack explicit structural modeling, while conventional graph neural networks tend to mix signals around binding/non-binding transition regions. In this paper, BC-GNN, a multi-channel graph neural network for nucleotide-level RNA-ligand binding-site prediction, is proposed. BC-GNN integrates sequence-informed auxiliary transition estimation, boundary-aware propagation (BAP), microenvironment-aware channel recalibration (MACR), and hierarchical multi-scale integration (HMSI) to improve structural representation learning. When evaluated on a benchmark derived from RNAmigos2 using the official leakage-controlled 0.75 split, BC-GNN achieves an AUC of 0.8280, an F1-score of 0.6086, and an MCC of 0.4511, outperforming multiple re-evaluated baselines under the same rigorous protocol. These results demonstrate that BC-GNN is effective for RNA-ligand binding-site prediction.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149008341","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

MSDW-Net: A Multi-Scale Network for Medical Image Registration Using Dynamic Wavelet Transform. 基于动态小波变换的医学图像配准多尺度网络MSDW-Net。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-16 DOI: 10.1007/s12539-026-00845-4

Bohua Chu, Baoju Zhang, Bo Zhang, Cuiping Zhang

{"title":"MSDW-Net: A Multi-Scale Network for Medical Image Registration Using Dynamic Wavelet Transform.","authors":"Bohua Chu, Baoju Zhang, Bo Zhang, Cuiping Zhang","doi":"10.1007/s12539-026-00845-4","DOIUrl":"https://doi.org/10.1007/s12539-026-00845-4","url":null,"abstract":"The clinical efficacy of modern medical imaging relies heavily on robust deformable registration for accurate diagnosis and treatment planning. Although deep learning has catalyzed significant progress in this domain, existing techniques encounter substantial hurdles in characterizing complex, non-rigid structural deformations. Addressing these limitations, this study introduces a novel 3D registration framework centered on a dynamic wavelet transform module. By adaptively tuning frequency decomposition filters and employing a frequency-sensitive multi-stream architecture, the proposed model enhances feature representation fidelity. The integration of a multi-scale paradigm further ensures the progressive and precise estimation of displacement fields. Furthermore, we optimize the widely used U-Net registration baseline by introducing a wavelet convolution module tailored to 3D images, thereby enhancing the extraction of global contextual information. To evaluate the model's efficacy, extensive comparisons were conducted using state-of-the-art architectures across four diverse, publicly available brain MRI repositories. The empirical evidence confirms that our method achieves superior registration fidelity, highlighting its potential for practical clinical implementation.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148982409","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

Beyond Structural Representations: A Semantic-Relational Graph Neural Network for Polypharmacy Side Effect Prediction. 超越结构表征：用于多药副作用预测的语义关系图神经网络。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-11 DOI: 10.1007/s12539-026-00877-w

Fatma Zahra Abdeldjouad, Menaouer Brahami

{"title":"Beyond Structural Representations: A Semantic-Relational Graph Neural Network for Polypharmacy Side Effect Prediction.","authors":"Fatma Zahra Abdeldjouad, Menaouer Brahami","doi":"10.1007/s12539-026-00877-w","DOIUrl":"https://doi.org/10.1007/s12539-026-00877-w","url":null,"abstract":"The concurrent use of several medications, known as polypharmacy, has increased in prevalence, especially among older adults with complex health conditions. Despite its effectiveness, this therapeutic strategy increases the risk of Polypharmacy Side Effects (PSEs), leading to substantial clinical and economic burden. Recent deep learning methods have integrated graph neural networks with pretrained language models to predict PSEs. However, these approaches rely on relation-agnostic decoders and fail to consider the pharmacological context of each PSE type. To address these issues, we propose SIREN, a semantic-relational graph neural network that integrates semantic embeddings from ChemBERTa and BioBERT with a multirelational biomedical graph structure. The model employs a relation-specific decoder to adapt the score of each drug pair based on the target side effect type. Experimental evaluation on the TWOSIDES dataset demonstrated high AUROC and AUPRC, outperforming current state-of-the-art models under the transductive setting and remaining competitive under inductive settings. Ablation studies further validated the importance of the relation-specific decoding mechanism and pre-trained node embeddings. By integrating semantic representations with graph-based reasoning, this study establishes an innovative framework for data-driven pharmacovigilance, thereby enhancing patient safety, reducing healthcare costs, and supporting regulatory compliance.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944793","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

HMHLVI: Hybrid Multi-view Hypergraph Learning with Variational Inference for snoRNA-Drug Association Prediction. HMHLVI：混合多视图超图学习与变分推理用于snorna -药物关联预测。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-04 DOI: 10.1007/s12539-026-00875-y

Tiyao Liu, Shudong Wang, Dapeng Wang, Shaoqiang Wang, Shanchen Pang

{"title":"HMHLVI: Hybrid Multi-view Hypergraph Learning with Variational Inference for snoRNA-Drug Association Prediction.","authors":"Tiyao Liu, Shudong Wang, Dapeng Wang, Shaoqiang Wang, Shanchen Pang","doi":"10.1007/s12539-026-00875-y","DOIUrl":"https://doi.org/10.1007/s12539-026-00875-y","url":null,"abstract":"Traditionally recognized for guiding rRNA modifications, small nucleolar RNAs (snoRNAs) are increasingly appreciated as key regulators of drug response. However, snoRNA and drug association data remain limited, and computational approaches capable of capturing their complex, high-order relationships are scarce. Here, we present HMHLVI, a hybrid multi-view hypergraph learning with variational inference framework that integrates sequence, structural, and association information to predict snoRNA-drug response associations. HMHLVI constructs three higher-order networks called attribute, topology, and association to model intrinsic features, structural dependencies, and known interactions, respectively. By employing shared and modality-specific hypergraph convolutional encoders together with an adaptive temperature-regulated multi-view attention mechanism, the framework effectively learns both common and view-specific biomolecular representations. In addition, a variational autoencoder is introduced to model the higher-order association network and to capture latent interaction patterns in a low-dimensional probabilistic space, enhancing robustness and denoising capability. Across three cross-validation settings, including random zero, multi-column zero, and multi-row zero, HMHLVI consistently outperformed other state-of-the-art models. System-level validations including functional enrichment, molecular docking, thermodynamic analysis, and clinical survival assessment confirmed its biological relevance. Key regulatory snoRNAs such as SNORD43 and SNORD116 were identified, and an integrated network linking drugs, diseases, snoRNAs, and target genes was constructed. Notably, SCARNA6 was predicted to modulate docetaxel response in esophageal squamous cell carcinoma, and higher SCARNA6 expression was associated with favorable overall survival in an exploratory Kaplan-Meier analysis (log-rank p = 0.0029).","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891605","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

A Re-parameterized Network-Based Lightweight Multimodal Fusion Network for Early Lung Cancer Risk Prediction. 基于重参数化网络的轻量级多模态融合网络用于早期肺癌风险预测。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-04 DOI: 10.1007/s12539-026-00873-0

Qifan Huang, Congjun Rao, Fuyan Hu, Xinping Xiao, Mark Goh

{"title":"A Re-parameterized Network-Based Lightweight Multimodal Fusion Network for Early Lung Cancer Risk Prediction.","authors":"Qifan Huang, Congjun Rao, Fuyan Hu, Xinping Xiao, Mark Goh","doi":"10.1007/s12539-026-00873-0","DOIUrl":"https://doi.org/10.1007/s12539-026-00873-0","url":null,"abstract":"Early detection of lung cancer using low-dose computed tomography (LDCT) is critical for improving patient outcomes. In this study, we propose a RepViT-based lightweight multimodal fusion network (Rep-LMFNet) framework, which integrates LDCT imaging and structured clinical information for risk prediction. The framework incorporates adaptive multi-scale module (ASMM), dynamic channel recalibration module (DCRM), adaptive feature enhancement module (AFEM), and hierarchical multimodal fusion module (HMFM) to improve feature representation and multimodal interaction. Experimental results under stratified five-fold cross-validation demonstrate that the proposed framework achieves an area under the receiver operating characteristic curve (AUC) of 0.877 (95% CI: 0.874-0.880), accuracy of 0.922 ± 0.002, sensitivity of 0.847 ± 0.010, specificity of 0.924 ± 0.002, and F1-score of 0.385 ± 0.010. Comparative experiments reveal that our model exhibits competitive advantages over several representative lightweight and hybrid architectures, while maintaining low computational cost with 11.9 M parameters and 1.9G floating-point operations per second (FLOPs). Multimodal learning consistently outperformed the single-modal configuration, indicating the effectiveness of integrating CT imaging with clinical features for early lung cancer risk assessment. The proposed framework provides an effective and computationally efficient solution for computer-aided early lung cancer screening.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891540","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

IIC-DTI: A Contrastive Learning Enhanced Inter-Intra Molecular Fusing Framework for Drug-Target Interaction Prediction. IIC-DTI：用于药物-靶标相互作用预测的对比学习增强的分子间融合框架。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2026-02-03 DOI: 10.1007/s12539-025-00799-z

Fei Wang, Dacheng Ruan, Yang Zhang, Yue Chen, Xiujuan Lei, Fang-Xiang Wu, Yansen Su, Chunhou Zheng

{"title":"IIC-DTI: A Contrastive Learning Enhanced Inter-Intra Molecular Fusing Framework for Drug-Target Interaction Prediction.","authors":"Fei Wang, Dacheng Ruan, Yang Zhang, Yue Chen, Xiujuan Lei, Fang-Xiang Wu, Yansen Su, Chunhou Zheng","doi":"10.1007/s12539-025-00799-z","DOIUrl":"10.1007/s12539-025-00799-z","url":null,"abstract":"Purpose: Predicting drug-target interactions (DTIs) is a practical demand in drug development and drug repositioning. Therefore, developing accurate and efficient DTI prediction methods has significant application value. Current models focus on the features of either drugs or targets independently, and concatenate them together for downstream prediction. They ignore the hidden associations between drugs and targets, which may affect the implementation of DTIs.Methods: In this work, we design a contrastive learning model to fuse intramolecular and intermolecular features of drugs and targets, named IIC-DTI. The intramolecular features focus on drug chemical structures and target amino acid sequences, which are generated separately. Meanwhile, the intermolecular features are focused on drug-target pairs, extracted by a multi-head cross-attention network. For the two embeddings of either a drug or a target in two views, a contrastive learning module is applied to update the embedding of one view by fusing information from the other view. Those novel embeddings are concatenated and fed into a 3-hidden layer neural network for predicting potential DTIs.Results: Multiple comparative experiments show that our proposed model has better performance than nine state-of-the-art methods, including two pre-trained large language models, according to several evaluation metrics on four benchmark datasets. In case study, 16 out of 20 drug-target pairs were verified by literature evidence. Moreover, IIC-DTI identified related interactions of a given drug and target successfully. It indicates that IIC-DTI has the potential application to identify DTIs in realistic conditions.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1165-1178"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113081","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

GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction. GSF-DTA：一种用于药物靶点亲和力预测的创新图序列融合框架。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-11-13 DOI: 10.1007/s12539-025-00782-8

Guiyang Zhang, Yuemei Wang, Danni Zhao, Pengmian Feng, Ting Zhang, Huachao Bin, Wei Chen

{"title":"GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction.","authors":"Guiyang Zhang, Yuemei Wang, Danni Zhao, Pengmian Feng, Ting Zhang, Huachao Bin, Wei Chen","doi":"10.1007/s12539-025-00782-8","DOIUrl":"10.1007/s12539-025-00782-8","url":null,"abstract":"Drug development is a lengthy and intricate process, where predicting drug-target affinity (DTA) is a vital step. Although traditional experimental techniques yield accurate and reliable results, their high cost and limited throughput render them impractical for large-scale applications. In contrast, computational approaches offer notable advantages in terms of scalability and operational efficiency. However, most existing models focus solely on either sequence information or molecular graph structure, limiting their capacity to capture the multifaceted nature of drug-target interactions. In the present work, we propose GSF-DTA, a novel graph-sequence fusion framework for DTA prediction. GSF-DTA integrates graph-based structural features and sequence-derived semantic representations to capture the interplay between drugs and targets. Quantitative evaluations demonstrate that GSF-DTA achieves superior predictive accuracy and exhibits strong generalization capabilities on the large-scale BindingDB dataset. Notably, GSF-DTA demonstrates robust performance in cold-start scenarios, enabling effective prediction for previously unseen drugs or targets. Extensive ablation studies and interpretability analyses further validate the effectiveness and transparency of our approach. Overall, GSF-DTA provides a promising and generalizable strategy for improving DTA prediction accuracy, contributing to the acceleration of drug design and discovery.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"866-878"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145512649","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

VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder. 基于异构图变分自编码器的药物-靶标相互作用预测模型。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-08-21 DOI: 10.1007/s12539-025-00758-8

Chen Zhang, Jiaqi Sun, Linlin Xing, Longbo Zhang, Hongzhen Cai, Kai Che

{"title":"VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder.","authors":"Chen Zhang, Jiaqi Sun, Linlin Xing, Longbo Zhang, Hongzhen Cai, Kai Che","doi":"10.1007/s12539-025-00758-8","DOIUrl":"10.1007/s12539-025-00758-8","url":null,"abstract":"Identifying drug-target interaction (DTI) is crucial for drug discovery and repositioning. Biological identification of DTI is costly and time-consuming, so heterogeneous network methods for DTI prediction, which can speed up drug development, have drawn much attention. However, the scarcity of known drug-target pairs leads to an overwhelmingly sparse network structure. This sparsity makes it difficult for graph convolutional networks to fully capture the potential relationships between nodes during information transmission and feature extraction, thus challenging the prediction performance of DTI. Therefore, we propose a method, called VHGAE, based on a heterogeneous graph variational autoencoder to predict drug-target interactions. The VHGAE model integrates a wealth of prior knowledge from various sources related to drugs and targets to construct a heterogeneous network. To address the sparsity issue within the constructed heterogeneous network, first, the weighted k-nearest neighbor algorithm is used to densify the DTI network to increase the connectivity of the sparse network. Second, the weighted graph convolutional network within a variational graph autoencoder framework is employed to bolster the edge weights with limited connections. Third, a variational expectation maximization algorithm is introduced between the encoder and the decoder to recover potential relationships in the sparse network. The experimental results on two datasets show that the performance of VHGAE outperforms nine state-of-the-art DTI prediction methods, which suggests that the design of VHGAE in multi-source data fusion and sparse network processing plays an important role in improving prediction performance.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1060-1079"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144953073","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 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub 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":"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":"986-1009"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","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

Advanced Multi-Level Bidirectional Attention Network for Retinal Vessel Segmentation. 用于视网膜血管分割的先进多级双向注意网络。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-12-12 DOI: 10.1007/s12539-025-00793-5

Zhendi Ma, Xiaobo Li, Yuxin Zhao, Jiahao Wang, Zhongmei Han, Hui Wang

{"title":"Advanced Multi-Level Bidirectional Attention Network for Retinal Vessel Segmentation.","authors":"Zhendi Ma, Xiaobo Li, Yuxin Zhao, Jiahao Wang, Zhongmei Han, Hui Wang","doi":"10.1007/s12539-025-00793-5","DOIUrl":"10.1007/s12539-025-00793-5","url":null,"abstract":"Retinal vessel segmentation is crucial for clinical diagnosis due to the rich morphological information in retinal fundus images. Although neural networks perform well, issues like feature loss during encoding and insufficient context fusion in skip connections remain. The complex curvature of small vessels and uneven background brightness further complicate pathological image segmentation. To address these problems, this paper proposes a multi-level bidirectional attention aggregation network. The encoder proposes a Partial Encoder Block (PEB) to reduce feature loss from traditional convolution. A Dynamic Direction Attention Module (DDAM) is proposed in the skip connection to enhance anisotropic geometric representation, preserving fine vessel details and contextual information. Additionally, a Multi-Feature Fusion Module (MFFM) is proposed to fuse multi-level features, retaining details while suppressing background noise. Experiments on DRIVE, STARE, and CHASEDB1 datasets demonstrate the network's effectiveness. On DRIVE, AUC, F1-score, and Sensitivity improved by 0.19%, 0.43%, and 1.17%, respectively. On STARE, AUC, F1, and sensitivity rose by 0.26%, 2.95%, and 2.07%, respectively. On CHASEDB1, AUC, F1-score, and specificity increased by 0.2%, 1.12%, and 0.44%, respectively. Results show the proposed network outperforms existing methods in segmentation performance.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"967-985"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145742169","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