Interdisciplinary Sciences: Computational Life Sciences最新文献

ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra. 基于resnext的自顶向下质谱中蛋白质形态表征的评分模型。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-05-17 DOI: 10.1007/s12539-025-00701-x

Jiancheng Zhong, Yicheng Luo, Chen Yang, Maoqi Yuan, Shaokai Wang

{"title":"ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra.","authors":"Jiancheng Zhong, Yicheng Luo, Chen Yang, Maoqi Yuan, Shaokai Wang","doi":"10.1007/s12539-025-00701-x","DOIUrl":"https://doi.org/10.1007/s12539-025-00701-x","url":null,"abstract":"In top-down proteomics, the accurate identification and characterization of proteoform through mass spectrometry represents a critical objective. As a result, achieving accuracy in identification results is essential. Multiple primary structure alterations in proteins generate a diverse range of proteoforms, resulting in an exponential increase in potential proteoform. Moreover, the absence of a definitive reference set complicates the standardization of results. Therefore, enhancing the accuracy of proteoform characterization continues to be a significant challenge. We introduced a ResNeXt-based deep learning model, PrSMBooster, for rescoring proteoform spectrum matches (PrSM) during proteoform characterization. As an ensemble method, PrSMBooster integrates four machine learning models, logistic regression, XGBoost, decision tree, and support vector machine, as weak learners to obtain PrSM features. The basic and latent features of PrSM are subsequently input into the ResNeXt model for final rescoring. To verify the effect and accuracy of the PrSMBooster model in rescoring proteoform characterization, it was compared with the characterization algorithm TopPIC across 47 independent mass spectrometry datasets from various species. The experimental results indicate that in most mass spectrometry datasets, the number of PrSMs obtained after rescoring with PrSMBooster increases at a false discovery rate (FDR) of 1%. Further analysis of the experimental results confirmed that PrSMBooster improves the accuracy of PrSM scoring, generates more mass spectrometry characterization results, and demonstrates strong generalization ability.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086199","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 CircRNA-Disease Associations Based on Heterogeneous Graph Neural Network and Knowledge Graph Attribute Mining Attention. 基于异构图神经网络和知识图属性挖掘注意力的circrna -疾病关联预测。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-05-13 DOI: 10.1007/s12539-025-00706-6

Wei Lan, Cong Peng, Hongyu Zhang, Chunling Li, Qingfeng Chen, Xin Xiao, Zhiqiang Wang

{"title":"Predicting CircRNA-Disease Associations Based on Heterogeneous Graph Neural Network and Knowledge Graph Attribute Mining Attention.","authors":"Wei Lan, Cong Peng, Hongyu Zhang, Chunling Li, Qingfeng Chen, Xin Xiao, Zhiqiang Wang","doi":"10.1007/s12539-025-00706-6","DOIUrl":"https://doi.org/10.1007/s12539-025-00706-6","url":null,"abstract":"The exploration of associations between circular RNAs (circRNAs) and diseases contributes to a deeper understanding of the pathogenesis of diseases. Many computational methods have been proposed for circRNA-disease associations identification. However, these methods still exhibit some limitations such as ignoring the effect of noise. In this paper, we proposed a new knowledge graph attribute mining attention network (KAATCDA) to predict circRNA-disease associations based on knowledge graph attribute network (KGA) and attribute mining attention network (AMA). Firstly, KGA is used to learn the feature representation of diseases. Then, the features of circRNAs are obtained using AMA, which are similar to disease feature representations. Finally, the scores of circRNA-disease associations are predicted based on circRNA feature representation and disease feature representation. Experiments of five-fold cross-validation on two datasets demonstrate that KAATCDA outperforms other state-of-the-art methods. In addition, the case study shows our method can effectively predict unknown circRNA-disease associations.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010476","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

iEnhancer-GDM: A Deep Learning Framework Based on Generative Adversarial Network and Multi-head Attention Mechanism to Identify Enhancers and Their Strength. 基于生成对抗网络和多头注意机制的深度学习框架，用于识别增强器及其强度。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-05-07 DOI: 10.1007/s12539-025-00703-9

Xiaomei Yang, Meng Liao, Bin Ye, Junfeng Xia, Jianping Zhao

{"title":"iEnhancer-GDM: A Deep Learning Framework Based on Generative Adversarial Network and Multi-head Attention Mechanism to Identify Enhancers and Their Strength.","authors":"Xiaomei Yang, Meng Liao, Bin Ye, Junfeng Xia, Jianping Zhao","doi":"10.1007/s12539-025-00703-9","DOIUrl":"https://doi.org/10.1007/s12539-025-00703-9","url":null,"abstract":"Enhancers are short DNA fragments capable of significantly increase the frequency of gene transcription. They often exert their effects on targeted genes over long distances, either in cis or in trans configurations. Identifying enhancers poses a challenge due to their variable position and sensitivities. Genetic variants within enhancer regions have been implicated in human diseases, highlighting critical importance of enhancers identification and strength prediction. Here, we develop a two-layer predictor named iEnhancer-GDM to identify enhancers and to predict enhancer strength. To address the challenges posed by the limited size of enhancer training dataset, which could cause issues such as model overfitting and low classification accuracy, we introduce a Wasserstein generative adversarial network (WGAN-GP) to augment the dataset. We employ a dna2vec embedding layer to encode raw DNA sequences into numerical feature representations, and then integrate multi-scale convolutional neural network, bidirectional long short-term memory network and multi-head attention mechanism for feature representation and classification. Our results validate the effectiveness of data augmentation in WGAN-GP. Our model iEnhancer-GDM achieves superior performance on an independent test dataset, and outperforms the existing models with improvements of 2.45% for enhancer identification and 11.5% for enhancer strength prediction by benchmarking against current methods. iEnhancer-GDM advances the precise enhancer identification and strength prediction, thereby helping to understand the functions of enhancers and their associations on genomics.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012930","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

ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction. ScAGCN：基于自适应聚合机制的scRNA-seq数据降维图卷积网络。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-04-25 DOI: 10.1007/s12539-025-00702-w

Xiaoshu Zhu, Liquan Zhao, Fei Teng, Shuang Meng, Miao Xie

{"title":"ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction.","authors":"Xiaoshu Zhu, Liquan Zhao, Fei Teng, Shuang Meng, Miao Xie","doi":"10.1007/s12539-025-00702-w","DOIUrl":"https://doi.org/10.1007/s12539-025-00702-w","url":null,"abstract":"With the development of single-cell RNA-sequencing (scRNA-seq) technology, scRNA-seq data analysis suffers huge challenges due to large scale, high dimensionality, high noise, and high sparsity. To achieve accurately embedded representation in the large-scale scRNA-seq data, we try to design a novel graph convolutional network with an adaptive aggregation mechanism. Based on the assumption that the aggregation order of different cells would be different, a graph convolutional network with an adaptive aggregation-based dimensionality reduction algorithm for scRNA-seq data is developed, named scAGCN. In scAGCN, a preprocessing consisting of quality control and feature selection is implemented. Then, an approximate nearest neighbor graph is rapidly constructed. Finally, a graph convolutional network with an adaptive aggregation mechanism is constructed, in which the neighborhood selection strategy based on node distribution and similarity boxplots is designed, and the aggregation function is optimized by defining a similarity measurement between neighborhood nodes and the central node. The results show that scAGCN outperforms existing dimensionality reduction methods on 15 real scRNA-seq datasets, especially in 10 large-scale scRNA-seq datasets.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995198","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

Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma. 基于途径富集的无监督学习识别胰腺导管腺癌中癌症相关成纤维细胞的新亚型。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-04-24 DOI: 10.1007/s12539-025-00705-7

Hongjing Ai, Rongfang Nie, Xiaosheng Wang

{"title":"Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.","authors":"Hongjing Ai, Rongfang Nie, Xiaosheng Wang","doi":"10.1007/s12539-025-00705-7","DOIUrl":"https://doi.org/10.1007/s12539-025-00705-7","url":null,"abstract":"Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms the single-cell gene expression matrix into a pathway enrichment matrix and generates its latent feature matrix. Based on the latent feature matrix, scPathClus clusters single cells using the method of community detection. Applying scPathClus to pancreatic ductal adenocarcinoma (PDAC) scRNA-seq datasets, we identified two types of cancer-associated fibroblasts (CAFs), termed csCAFs and gapCAFs, which highly expressed complement system and gap junction-related pathways, respectively. Spatial transcriptome analysis revealed that gapCAFs and csCAFs are located at cancer and non-cancer regions, respectively. Pseudotime analysis suggested a potential differentiation trajectory from csCAFs to gapCAFs. Bulk transcriptome analysis showed that gapCAFs-enriched tumors are more endowed with tumor-promoting characteristics and worse clinical outcomes, while csCAFs-enriched tumors confront stronger antitumor immune responses. Compared to established CAF subtyping methods, this method displays better prognostic relevance.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010199","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