Interdisciplinary Sciences: Computational Life Sciences最新文献

{"title":"HiSVision: A Method for Detecting Large-Scale Structural Variations Based on Hi-C Data and Detection Transformer.","authors":"Haixia Zhai, Chengyao Dong, Tao Wang, Junwei Luo","doi":"10.1007/s12539-024-00677-0","DOIUrl":"10.1007/s12539-024-00677-0","url":null,"abstract":"<p><p>Structural variation (SV) is an important component of the diversity of the human genome. Many studies have shown that SV has a significant impact on human disease and is strongly associated with the development of cancer. In recent years, the Hi-C sequencing technique has been shown to be useful for detecting large-scale SVs, and several methods have been proposed for identifying SVs from Hi-C data. However, due to the complexity of the 3D genome structure, accurate identifying SVs from the Hi-C contact matrix remains a challenging task. Here, we present HiSVision, a method for identifying large-scale SVs from Hi-C data using a detection transformer framework. Inspired by object detection network, we transform the Hi-C contact matrix into images, then identify candidate SV regions on the image by detection transformer, and finally filter SVs based on features around the breakpoints. Experimental results show that HiSVision outperforms existing methods in terms of precision and F1 score on cancer cell lines and simulated datasets. The source code and data are available from https://github.com/dcy99/HiSVision .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"519-527"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876974","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":"Towards Reliable Healthcare Imaging: A Multifaceted Approach in Class Imbalance Handling for Medical Image Segmentation.","authors":"Lijuan Cui, Mingquan Xu, Chao Liu, Tianyu Liu, Xiaoting Yan, Yan Zhang, Xiaofeng Yang","doi":"10.1007/s12539-025-00726-2","DOIUrl":"10.1007/s12539-025-00726-2","url":null,"abstract":"<p><p>Class imbalance is a dominant challenge in medical image segmentation when dealing with MRI images from highly imbalanced datasets. This study introduces a comprehensive, multifaceted approach to enhance the accuracy and reliability of segmentation models under such conditions. Our model integrates advanced data augmentation, innovative algorithmic adjustments, and novel architectural features to address class label distribution effectively. To ensure the multiple aspects of training process, we have customized the data augmentation technique for medical imaging with multi-dimensional angles. The multi-dimensional augmentation technique helps to reduce the bias towards majority classes. We have implemented novel attention mechanisms, i.e., Enhanced Attention Module (EAM) and spatial attention. These attention mechanisms enhance the focus of the model on the most relevant features. Further, our architecture incorporates a dual decoder system and Pooling Integration Layer (PIL) to capture accurate foreground and background details. We also introduce a hybrid loss function, which is designed to handle the class imbalance by guiding the training process. For experimental purposes, we have used multiple datasets such as Digital Database Thyroid Image (DDTI), Breast Ultrasound Images Dataset (BUSI) and LiTS MICCAI 2017 to demonstrate the prowess of the proposed network using key evaluation metrics, i.e., IoU, Dice coefficient, precision, and recall.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"614-633"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144583786","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":"A Novel Drug-Disease Association Prediction Method Based on Deep Non-Negative Matrix Factorization with Local Graph Feature.","authors":"Mengyun Yang, Bin Yang, Jiajun Chen, Xiwei Tang, Guihua Duan","doi":"10.1007/s12539-025-00733-3","DOIUrl":"10.1007/s12539-025-00733-3","url":null,"abstract":"<p><p>Computational drug repurposing utilizes data analysis and predictive models to identify new uses for existing drugs and new drugs, significantly improving research efficiency and reducing costs compared to traditional screening methods. Due to the limitations of current computational models in extracting deep key features, we develop a novel drug repurposing model based on the deep non-negative matrix factorization (DNMF-DDA) to enhance the accuracy of drug-disease association predictions. The model leverages similarity and known association data to extract low-rank features from complex data spaces, allowing for the prediction of potential drug-disease associations. To improve performance for novel drugs, we apply the k-nearest neighbors (KNN) algorithm for preprocessing, increasing the density of the matrix's prior information. Next, we construct two integrated matrices based on the similarities of drugs and diseases, respectively, and the optimized association data. During deep matrix factorization, we incorporate graph Laplacian and relaxed regularization constraints to optimize local graph features. This multi-layer optimization enhances the model's understanding of complex drug-disease relationships, effectively mitigating the negative impact of insufficient prior information during cold-start tests. Furthermore, we incorporate non-negativity constraints to ensure that the prediction results are biologically meaningful. To evaluate the performance of DNMF-DDA, we conducted cold-start test and 10-fold cross-validation on three datasets and systematically compared it with five state-of-the-art drug repurposing methods. The results demonstrate that DNMF-DDA performs exceptionally well in predicting drug-disease associations, significantly outperforming existing approaches. Our proposed method not only efficiently handles high-dimensional data but also exhibits superior performance, providing new insights for drug development. Moreover, the case study further validated the significant practical value of the DNMF-DDA model in practical applications.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"598-613"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144583785","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":"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":"10.1007/s12539-025-00703-9","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"662-672"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","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}

{"title":"NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.","authors":"Fan Zhang, Chaoyang Liu, Binjie Wang, Xiaopan Chen, Xinhong Zhang","doi":"10.1007/s12539-025-00716-4","DOIUrl":"10.1007/s12539-025-00716-4","url":null,"abstract":"<p><p>Non-coding RNAs (ncRNAs) are one of the components of epigenetic mechanisms that regulates gene expression. Studying ncRNA-protein interactions (NPI) can help to explore a wide range of biological features and related diseases. Traditional NPI research methods often require expensive equipment, a lot of time and labor. With the abundant samples accumulated from traditional experiments, remarkable progress has been made in the study of NPI by computational methods. Heterogeneous graph neural network is a deep learning method that synthesizes heterogeneous types of data as well as network topology. In this study, we propose an NPI-HetGNN model for NPI prediction based on heterogeneous graph neural networks. Firstly, initial features are constructed by integrating the sequence properties of ncRNA and protein data as well as the topology of heterogeneous connections. Then, the multilevel homogeneous subgraph is obtained and its semantic information is aggregated by metapath walking. At the same time, the homogeneous node information is fused within the subgraph metapath. To enhance feature extraction ability of the network, an energy-constrained self-attention module is introduced. Due to the lack of wet lab validation conditions, this study adopts computational verification. The performance of the NPI-HetGNN model on four benchmark datasets is experimentally verified. Ablation experiments also confirmed the comprehensiveness and validity of our model design. The experimental results show that comparing with six state-of-the-art methods, our NPI-HetGNN achieves very satisfactory results on all four datasets.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"730-743"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198997","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":"Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.","authors":"Yaowen Gu, Zidu Xu, Carl Yang","doi":"10.1007/s12539-024-00654-7","DOIUrl":"10.1007/s12539-024-00654-7","url":null,"abstract":"<p><p>Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to predict DDAs, achieving notable performances compared to traditional machine learning and matrix factorization approaches. However, these methods depend heavily on network topology, hampered by incomplete and noisy network data, and overlook the wealth of biomedical knowledge available. Correspondingly, large language models (LLMs) excel in graph search and relational reasoning, which can possibly enhance the integration of comprehensive biomedical knowledge into drug and disease profiles. In this study, we first investigate the contribution of LLM-inferred knowledge representation in drug repositioning and DDA prediction. A zero-shot prompting template was designed for LLM to extract high-quality knowledge descriptions for drug and disease entities, followed by embedding generation from language models to transform the discrete text to continual numerical representation. Then, we proposed LLM-DDA with three different model architectures (LLM-DDA_{Node Feat}, LLM-DDA_{Dual GNN}, LLM-DDA_GNN-AE) to investigate the best fusion mode for LLM-based embeddings. Extensive experiments on four DDA benchmarks show that, LLM-DDA_GNN-AE achieved the optimal performance compared to 11 baselines with the overall relative improvement in AUPR of 23.22%, F1-Score of 17.20%, and precision of 25.35%. Meanwhile, selected case studies of involving Prednisone and Allergic Rhinitis highlighted the model's capability to identify reliable DDAs and knowledge descriptions, supported by existing literature. This study showcases the utility of LLMs in drug repositioning with its generality and applicability in other biomedical relation prediction tasks.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"698-715"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346018","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":"SpatialCVGAE: Consensus Clustering Improves Spatial Domain Identification of Spatial Transcriptomics Using VGAE.","authors":"Jinyun Niu, Fangfang Zhu, Donghai Fang, Wenwen Min","doi":"10.1007/s12539-024-00676-1","DOIUrl":"10.1007/s12539-024-00676-1","url":null,"abstract":"<p><p>The advent of spatially resolved transcriptomics (SRT) has provided critical insights into the spatial context of tissue microenvironments. Spatial clustering is a fundamental aspect of analyzing spatial transcriptomics data. However, spatial clustering methods often suffer from instability caused by the sparsity and high noise in the SRT data. To address this challenge, we propose SpatialCVGAE, a consensus clustering framework designed for SRT data analysis. SpatialCVGAE adopts the expression of high-variable genes from different dimensions along with multiple spatial graphs as inputs to variational graph autoencoders (VGAEs), learning multiple latent representations for clustering. These clustering results are then integrated using a consensus clustering approach, which enhances the model's stability and robustness by combining multiple clustering outcomes. Experiments demonstrate that SpatialCVGAE effectively mitigates the instability typically associated with non-ensemble deep learning methods, significantly improving both the stability and accuracy of the results. Compared to previous non-ensemble methods in representation learning and post-processing, our method fully leverages the diversity of multiple representations to accurately identify spatial domains, showing superior robustness and adaptability. All code and public datasets used in this paper are available at https://github.com/wenwenmin/SpatialCVGAE .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"497-518"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142828461","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":"NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions.","authors":"Xin Zhang, Haofeng Ma, Sizhe Wang, Hao Wu, Yu Jiang, Quanzhong Liu","doi":"10.1007/s12539-025-00689-4","DOIUrl":"10.1007/s12539-025-00689-4","url":null,"abstract":"<p><p>Accurate identification of ncRNA-protein interactions (NPIs) is critical for understanding various cellular activities and biological functions of ncRNAs and proteins. Many sequence- and/or structure- and graph-based computational approaches have been developed to identify NPIs from large-scale ncRNA and protein data in a high-throughput manner. However, many sequence- and/or structure- and graph-based computational approaches often ignore either the topological information in NPIs or the influence of other molecule networks on NPI prediction. In this work, we propose NPI-HGNN, an end-to-end graph neural network (GNN)-based approach for the identification of NPIs from a large heterogeneous network, consisting of the ncRNA-protein interaction network, the ncRNA-ncRNA similarity network, and the protein-protein interaction network. To our knowledge, NPI-HGNN is the first GNN-based predictor that integrates related heterogeneous networks for NPI prediction. Experiments on five benchmarking datasets demonstrate that NPI-HGNN outperformed several state-of-the-art sequence- and/or structure- and graph-based predictors. In addition, we showcased the prediction power of NPI-HGNN by identifying 12 interacting ncRNAs of the pre-mRNA 3' end processing protein, which indicates the effectiveness of the proposed model. The source code of NPI-HGNN is freely available for academic purposes at https://github.com/zhangxin11111/NPI-HGNN .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"649-661"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143467996","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":"MLWNNR: LncRNA-Disease Association Prediction with Multi-Kernel Learning-Driven Weighted Nuclear Norm Regularization.","authors":"Guo-Bo Xie, Hao-Jie Xu, Guo-Sheng Gu, Zhi-Yi Lin, Jun-Rui Yu, Rui-Bin Chen","doi":"10.1007/s12539-025-00717-3","DOIUrl":"10.1007/s12539-025-00717-3","url":null,"abstract":"<p><p>Emerging evidence highlights long non-coding RNAs (lncRNAs) as pivotal regulators demonstrating significant linkages with diverse human pathologies through expression dynamics and regulatory cascades. This research endeavors to establish an algorithm for forecasting the associations between lncRNAs and diseases based on multi-kernel learning-driven weighted nuclear norm regularization (MLWNNR). Specifically, our framework first uses a kernel learning algorithm centered on k-nearest neighbors to integrate multi-similarity kernels. Then, we construct a heterogeneous lncRNA-disease associations network utilizing similarity information and confirm lncRNA-disease associations. Finally, we adopt weighted nuclear norm regularization to complete the heterogeneous network to derive the final association prediction score. MLWNNR achieves impressive performance on three datasets and outperforms six representative models in the comparative experiments, which demonstrates its robustness and excellent generalization abilities. Furthermore, in case studies centered on three common human diseases, the majority of the hypothesized connections are corroborated by experimental literature. MLWNNR is a reliable approach for inferring lncRNA-disease associations, according to the experimental results.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"673-690"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144475105","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":"MTGGF: A Metabolism Type-Aware Graph Generative Model for Molecular Metabolite Prediction.","authors":"Peng-Cheng Zhao, Xue-Xin Wei, Qiong Wang, Hao-Yang Wang, Bing-Xue Du, Jia-Ning Li, Bei Zhu, Hui Yu, Jian-Yu Shi","doi":"10.1007/s12539-024-00681-4","DOIUrl":"10.1007/s12539-024-00681-4","url":null,"abstract":"<p><p>Metabolism in vivo turns small molecules (e.g., drugs) into metabolites (new molecules), which brings unexpected safety issues in drug development. However, it is costly to determine metabolites by biological assays. Recent computational methods provide new promising approaches by predicting possible metabolites. Rule-based methods utilize predefined reaction-derived rules to infer metabolites. However, they are powerless to new metabolic reaction patterns. In contrast, rule-free methods leverage sequence-to-sequence machine translation to generate metabolites. Nevertheless, they are insufficient to characterize molecule structures, and bear weak interpretability. To address these issues in rule-free methods, this manuscript proposes a novel metabolism type-aware graph generative framework (MTGGF) for molecular metabolite prediction. It contains a two-stage learning process, including a pre-training on a large general chemical reaction dataset, and a fine-tuning on three smaller type-specific metabolic reaction datasets. Its core, an elaborate graph-to-graph generative model, treats both atoms and bonds as bipartite vertices, and molecules as bipartite graphs, such that it can embed rich information of molecule structures and ensure the integrity of generated metabolite structures. The comparison with state-of-the-art methods demonstrates its superiority. Furthermore, the ablation study validates the contributions of its two graph encoding components and its reaction-type-specific fine-tuning models. More importantly, based on interactive attention between a molecule and its metabolites, the case studies on five approved drugs reveal that there exist crucial substructures specific to metabolism types. It is anticipated that this framework can boost the risk evaluation of drug metabolites. The codes are available at https://github.com/zpczaizheli/Metabolite .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"528-540"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931780","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}