Interdisciplinary Sciences: Computational Life Sciences最新文献_第10页

Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph. 利用时空图神经网络和基于三维结构的复杂图的融合模型预测蛋白质配体结合亲和力

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2024-11-14 DOI: 10.1007/s12539-024-00644-9

Gaili Li, Yongna Yuan, Ruisheng Zhang

{"title":"Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.","authors":"Gaili Li, Yongna Yuan, Ruisheng Zhang","doi":"10.1007/s12539-024-00644-9","DOIUrl":"10.1007/s12539-024-00644-9","url":null,"abstract":"The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network designed to study protein-ligand interactions based on the 3D structural data of protein-ligand complexes. Unlike 1D protein sequences or 2D ligand graphs, the 3D graph representation offers a more precise portrayal of the complex interactions between proteins and ligands. Research studies have shown that our fusion model, PLA-STGCNnet, outperforms individual algorithms in accurately predicting binding affinity. The advantage of a fusion model is the ability to fully combine the advantages of multiple different models and improve overall performance by combining their features and outputs. Our fusion model shows satisfactory performance on different data sets, which proves its generalization ability and stability. The fusion-based model showed good performance in protein-ligand affinity prediction, and we successfully applied the model to drug screening. Our research underscores the promise of fusion spatial-temporal graph neural networks in addressing complex challenges in protein-ligand affinity prediction. The Python scripts for implementing various model components are accessible at https://github.com/ligaili01/PLA-STGCN.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"257-276"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619766","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

iAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming. iAmyP：基于序列最小二乘法编程的淀粉样蛋白六肽识别多视角学习。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2024-11-15 DOI: 10.1007/s12539-024-00666-3

Jinling Cai, Jianping Zhao, Yannan Bin, Junfeng Xia, Chunhou Zheng

{"title":"iAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming.","authors":"Jinling Cai, Jianping Zhao, Yannan Bin, Junfeng Xia, Chunhou Zheng","doi":"10.1007/s12539-024-00666-3","DOIUrl":"10.1007/s12539-024-00666-3","url":null,"abstract":"The development of peptide drug is hindered by the risk of amyloidogenic aggregation; if peptides tend to aggregate in this manner, they may be unsuitable for drug design. Computational methods aimed at predicting amyloidogenic sequences often face challenges in extracting high-quality features, and their predictive performance can be enchanced. To surmount these challenges, iAmyP was introduced as a specialized computational tool designed for predicting amyloidogenic hexapeptides. Utilizing multi-view learning, iAmyP incorporated sequence, structural, and evolutionary features, performing feature selection and feature fusion through recursive feature elimination and attention mechanisms. This amalgamation of features and subsequent feature selection and fusion lead to optimal performance facilitated by an optimization algorithm based on sequence least squares programming. Notably, iAmyP exhibited robust generalization for peptides with lengths of 7-10 amino acids. The role of hydrophobic amino acids in the aggregation process is critical, and a thorough analysis have significantly enhanced our insight into their significance in amyloidogenic hexapeptides. This tool represented an advancement in the development of peptide therapeutics by providing an understanding of amyloidogenic aggregation, establishing itself as a valuable framework for assessing amyloidogenic sequences. The data and code can be freely accessed at https://github.com/xialab-ahu/iAmyP .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"277-292"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638847","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

Prediction of Multimorbidity Network Evolution in Middle-Aged and Elderly Population Based on CE-GCN. 基于CE-GCN的中老年人群多病网络演化预测

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2025-02-10 DOI: 10.1007/s12539-024-00685-0

Yushi Che, Yiqiao Wang

{"title":"Prediction of Multimorbidity Network Evolution in Middle-Aged and Elderly Population Based on CE-GCN.","authors":"Yushi Che, Yiqiao Wang","doi":"10.1007/s12539-024-00685-0","DOIUrl":"10.1007/s12539-024-00685-0","url":null,"abstract":"Purpose: With the evolving disease spectrum, chronic diseases have emerged as a primary burden and a leading cause of mortality. Due to the aging population and the nature of chronic illnesses, patients often suffer from multimorbidity. Predicting the likelihood of these patients developing specific diseases in the future based on their current health status and age factors is a crucial task in multimorbidity research.Methods: We propose an algorithm, CE-GCN, which integrates age sequence and embeds Graph Convolutional Network (GCN) into Gated Recurrent Unit (GRU), utilizing the topological feature of network common neighbors to predict links in dynamic complex networks. First, we constructed a disease evolution network spanning from ages 45 to 90 years old using disease information from 3333 patients. Then, we introduced an innovative approach for link prediction aimed at uncovering relationships between various diseases. This method takes into account patients' age to construct the evolutionary structure of the disease network, thereby predicting the connections between chronic diseases.Results: Results from experiments conducted on real networks indicate that our model surpasses others regarding both MRR and MAP. The proposed method accurately reveals associations between diseases and effectively captures future disease risks.Conclusion: Our model can serve as an objective and convenient computer-aided tool to identify hidden relationships between diseases in order to assist healthcare professionals in taking early disease interventions, which can substantially lower the costs associated with treating multimorbidity and enhance the quality of life for patients suffering from chronic conditions.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"424-436"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12106578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390373","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}

引用次数: 0

Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach. 基于深度学习方法发现靶向 TLR4 治疗肝脏炎症疾病的银翘解毒片有效成分

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2024-11-19 DOI: 10.1007/s12539-024-00670-7

Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu

{"title":"Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach.","authors":"Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu","doi":"10.1007/s12539-024-00670-7","DOIUrl":"10.1007/s12539-024-00670-7","url":null,"abstract":"Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulating the Toll-like receptor 4 (TLR4) target. Therefore, a thorough exploration of the effective components and therapeutic mechanisms targeting TLR4 in YCHD is a promising strategy for liver diseases. In this study, the AIGO-DTI deep learning framework was proposed to predict the targeting probability of major components in YCHD for TLR4. Comparative evaluations with four machine learning models (RF, SVM, KNN, XGBoost) and two deep learning models (GCN, GAT) demonstrated that the AIGO-DTI framework exhibited the best overall performance, with Recall and AUC reaching 0.968 and 0.991, respectively.This study further utilized the AIGO-DTI model to identify the potential impact of Isoscopoletin, a major component of YCHD, on TLR4. Subsequent wet experiments revealed that Isoscopoletin could influence the maturation of Dendritic Cells (DCs) induced by Lipopolysaccharide (LPS) through TLR4, suggesting its therapeutic potential for liver diseases, especially hepatitis. Additionally, based on the AIGO-DTI framework, this study established an online platform named TLR4-Predict to facilitate domain experts in discovering more compounds related to TLR4. Overall, the proposed AIGO-DTI framework accurately predicts unique compounds in YCHD that interact with TLR4, providing new insights for identifying and screening lead compounds targeting TLR4.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"293-305"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667815","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

WDGBANDTI: A Deep Graph Convolutional Network-Based Bilinear Attention Network for Drug-Target Interaction Prediction with Domain Adaptation. 基于深度图卷积网络的药物-靶标相互作用预测双线性注意网络。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-05-23 DOI: 10.1007/s12539-025-00714-6

Nianrui Wang, Shumin Zhao, Ziwei Li, Jianqiang Sun, Ming Yi

{"title":"WDGBANDTI: A Deep Graph Convolutional Network-Based Bilinear Attention Network for Drug-Target Interaction Prediction with Domain Adaptation.","authors":"Nianrui Wang, Shumin Zhao, Ziwei Li, Jianqiang Sun, Ming Yi","doi":"10.1007/s12539-025-00714-6","DOIUrl":"https://doi.org/10.1007/s12539-025-00714-6","url":null,"abstract":"Backgrounds: During the development of new drugs, it is essential to assess their effectiveness and examine the potential mechanisms behind side effects. This process typically involves combining the analysis of drugs under development with relevant existing drugs to more precisely evaluate the effects of drugs and targets. The use of deep learning methods to analyze this problem is currently a research hotspot, but several limitations remain: (i) how to deepen the analysis from the molecular level to the atomic level and analyze the key substructures that affect interactions on the basis of pharmaceutical mechanisms; (ii) how to integrate biomedical analysis with deep learning methods to make it medically sound and enhance interpretability.Methods: To address the limitations of existing research, based on Deep Graph Convolutional Network (Deep-GCN) and Bilinear Attention Network (BAN), we have constructed an interpretable deep learning framework, WDGBANDTI, to analyze and predict drug‒target interactions at the substructure level and enhance the prediction capability of the model with respect to unidentified target pairings by adding modules.Results: For different application scenarios, we validated the model via several commonly used and highly covered datasets. We also selected several state-of-the-art computer methods as comparison objects, and our model demonstrates advantages in accuracy, sensitivity, specificity, and other deep learning features. More importantly, the model can identify the substructures that play a role in drug‒target interactions through BAN, highlighting its excellent interpretability.Conclusion: In conclusion, we believe that our work will contribute to advancements in drug development and side effect experiments and provide meaningful guidance for drug design.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132317","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

IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction. 用于药物-靶标相互作用预测的可解释混合深度特征融合网络。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-05-12 DOI: 10.1007/s12539-025-00712-8

Yuanyuan Zhang, Qihao Wang, Ci'ao Zhang, Baoming Feng, Junliang Shang, Li Zhang

{"title":"IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.","authors":"Yuanyuan Zhang, Qihao Wang, Ci'ao Zhang, Baoming Feng, Junliang Shang, Li Zhang","doi":"10.1007/s12539-025-00712-8","DOIUrl":"https://doi.org/10.1007/s12539-025-00712-8","url":null,"abstract":"Conventional drug discovery is expensive and takes a long period. Drug-target interaction (DTI) prediction through computational methods significantly improves efficiency and reduces costs, holding substantial research value. Despite progress in existing prediction methods, two major challenges remain: first, most methods fail to effectively combine shallow and deep features of protein sequences, overlooking the synergistic effect of both; second, existing feature fusion techniques are relatively simple and struggle to fully capture the complexity and richness of fused features. We suggest an interpretable hybrid deep feature fusion network (IHDFN) as a solution to these problems. In the hybrid deep feature extraction module for protein sequences, shallow and deep features of protein sequences are extracted through two distinct views respectively, which capture multi-level information of proteins comprehensively. To further enhance the feature fusion effect, we introduce the StarNet fusion model in this module, enabling efficient fusion of shallow and deep features and enriching feature representation. To further improve the representation power of drug characteristics and the stability of the model, we use a graph convolutional network (GCN) in the drug feature extraction section in conjunction with residual connections and layer normalization. Furthermore, by integrating multimodal features from drugs and proteins utilizing an attention mechanism in the heterogeneous feature fusion module, we increase the complexity of features and achieve interpretability in predictions by attention focusing. Finally, we experimented on three datasets, and the findings indicate that IHDFN has exceptional performance and robustness compared to other cutting-edge techniques, underscoring its great promise and usefulness in DTI tasks. The code for this study is available on GitHub at https://github.com/wangqhfff/IHDFN.git .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024434","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

Generation-Based Few-Shot BioNER via Local Knowledge Index and Dual Prompts. 基于局部知识索引和双提示的基于代的少射生物识别。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-05-10 DOI: 10.1007/s12539-025-00709-3

Weixin Li, Hong Wang, Wei Li, Jun Zhao, Yanshen Sun

{"title":"Generation-Based Few-Shot BioNER via Local Knowledge Index and Dual Prompts.","authors":"Weixin Li, Hong Wang, Wei Li, Jun Zhao, Yanshen Sun","doi":"10.1007/s12539-025-00709-3","DOIUrl":"https://doi.org/10.1007/s12539-025-00709-3","url":null,"abstract":"Few-shot Biomedical Named Entity Recognition (BioNER) presents significant challenges due to limited training data and the presence of nested and discontinuous entities. To tackle these issues, a novel approach GKP-BioNER, Generation-based Few-Shot BioNER via Local Knowledge Index and Dual Prompts, is proposed. It redefines BioNER as a generation task by integrating hard and soft prompts. Specifically, GKP-BioNER constructs a localized knowledge index using a Wikipedia dump, facilitating the retrieval of semantically relevant texts to the original sentence. These texts are then reordered to prioritize the most semantically relevant content to the input data, serving as hard prompts. This helps the model to address challenges demanding domain-specific insights. Simultaneously, GKP-BioNER preserves the integrity of the pre-trained models while introducing learnable parameters as soft prompts to guide the self-attention layer, allowing the model to adapt to the context. Moreover, a soft prompt mechanism is designed to support knowledge transfer across domains. Extensive experiments on five datasets demonstrate that GKP-BioNER significantly outperforms eight state-of-the-art methods. It shows robust performance in low-resource and complex scenarios across various domains, highlighting its strength in knowledge transfer and broad applicability.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963773","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

Identify Modules Associated with Immunotherapy Response from Mouse Tumor Profiles for Stratifying Cancer Patients. 从小鼠肿瘤谱中识别与免疫治疗反应相关的模块，用于分层癌症患者。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-05-09 DOI: 10.1007/s12539-025-00719-1

Dechen Xu, Jie Li, Li Zhou, Jiahuan Jin

{"title":"Identify Modules Associated with Immunotherapy Response from Mouse Tumor Profiles for Stratifying Cancer Patients.","authors":"Dechen Xu, Jie Li, Li Zhou, Jiahuan Jin","doi":"10.1007/s12539-025-00719-1","DOIUrl":"https://doi.org/10.1007/s12539-025-00719-1","url":null,"abstract":"Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical benefits in cancer treatment, but only a minority of patients exhibit favorable response, highlighting the importance of determining patients who will benefit from immunotherapy. Currently, patient datasets regarding immunotherapy response are scarce, while ample experiments can be performed on syngeneic mouse tumor models to generate valuable data. Therefore, how to effectively utilize mouse data to identify predictors of immunotherapy response and subsequently transfer relevant knowledge to predict human response to ICIs is a question worth studying. In this study, we propose a novel methodology to address this issue. Firstly, we identify gene modules associated with immunotherapy response from mouse tumor profiles based on cancer gene panels. Subsequently, these identified modules are employed to build prediction models for immunotherapy response based on mouse data. Furthermore, we transfer these models to predict ICIs responses of human cancer patients. Experimental results demonstrate that the gene modules identified from mouse data are reliable predictors of immunotherapy response. The mouse-based models built on these modules could be transferred to humans, effectively predicting drug responses and survival outcomes for cancer patients. Compared to conventional cancer biomarkers and existing prediction models based on mouse data, our method exhibits superior performance. These findings provide a valuable reference for further in-depth research on immunotherapy response prediction model based on mouse tumor profiles, with the potential for transfer applications in human cancer therapy.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143963774","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

Semantic Consistency Network with Edge Learner and Connectivity Enhancer for Cervical Tumor Segmentation from Histopathology Images. 基于边缘学习器和连接增强器的语义一致性网络在组织病理图像中分割子宫颈肿瘤。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-04-23 DOI: 10.1007/s12539-025-00691-w

Lisha Pang, Peng He, Yue Han, Hao Cui, Peng Feng, Chi Zhang, Pan Huang, Sukun Tian

{"title":"Semantic Consistency Network with Edge Learner and Connectivity Enhancer for Cervical Tumor Segmentation from Histopathology Images.","authors":"Lisha Pang, Peng He, Yue Han, Hao Cui, Peng Feng, Chi Zhang, Pan Huang, Sukun Tian","doi":"10.1007/s12539-025-00691-w","DOIUrl":"https://doi.org/10.1007/s12539-025-00691-w","url":null,"abstract":"Accurate tumor grading and regional identification of cervical tumors are important for diagnosis and prognosis. Traditional manual microscopy methods suffer from time-consuming, labor-intensive, and subjective bias problems, so tumor segmentation methods based on deep learning are gradually becoming a hotspot in current research. Cervical tumors have diverse morphologies, which leads to low similarity between the mask edge and ground-truth edge of existing semantic segmentation models. Moreover, the texture and geometric arrangement features of normal tissues and tumors are highly similar, which causes poor pixel connectivity in the mask of the segmentation model. To this end, we propose an end-to-end semantic consistency network with the edge learner and the connectivity enhancer, i.e., ERNet. First, the edge learner consists of a stacked shallow convolutional neural network, so it can effectively enhance the ability of ERNet to learn and represent polymorphic tumor edges. Second, the connectivity enhancer learns detailed information and contextual information of tumor images, so it can enhance the pixel connectivity of the masks. Finally, edge features and pixel-level features are adaptively coupled, and the segmentation results are additionally optimized by the tumor classification task as a whole. The results show that, compared with those of other state-of-the-art segmentation models, the structural similarity and the mean intersection over union of ERNet are improved to 88.17% and 83.22%, respectively, which reflects the excellent edge similarity and pixel connectivity of the proposed model. Finally, we conduct a generalization experiment on laryngeal tumor images. Therefore, the ERNet network has good clinical popularization and practical value.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006689","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

DSMR: Dual-Stream Networks with Refinement Module for Unsupervised Multi-modal Image Registration. 基于改进模块的无监督多模态图像配准双流网络。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-04-19 DOI: 10.1007/s12539-025-00707-5

Lei Li, Liumin Zhu, Qifu Wang, Zhuoli Dong, Tianli Liao, Peng Li

{"title":"DSMR: Dual-Stream Networks with Refinement Module for Unsupervised Multi-modal Image Registration.","authors":"Lei Li, Liumin Zhu, Qifu Wang, Zhuoli Dong, Tianli Liao, Peng Li","doi":"10.1007/s12539-025-00707-5","DOIUrl":"https://doi.org/10.1007/s12539-025-00707-5","url":null,"abstract":" Multi-modal medical image registration aims to align images from different modalities to establish spatial correspondences. Although deep learning-based methods have shown great potential, the lack of explicit reference relations makes unsupervised multi-modal registration still a challenging task. In this paper, we propose a novel unsupervised dual-stream multi-modal registration framework (DSMR), which combines a dual-stream registration network with a refinement module. Unlike existing methods that treat multi-modal registration as a uni-modal problem using a translation network, DSMR leverages the moving, fixed and translated images to generate two deformation fields. Specifically, we first utilize a translation network to convert a moving image into a translated image similar to a fixed image. Then, we employ the dual-stream registration network to compute two deformation fields respectively: the initial deformation field generated from the fixed image and the moving image, and the translated deformation field generated from the translated image and the fixed image. The translated deformation field acts as a pseudo-ground truth to refine the initial deformation field and mitigate issues such as artificial features introduced by translation. Finally, we use the refinement module to enhance the deformation field by integrating registration errors and contextual information. Extensive experimental results show that our DSMR achieves exceptional performance, demonstrating its strong generalization in learning the spatial relationships between images from unsupervised modalities. The source code of this work is available at https://github.com/raylihaut/DSMR .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985663","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