Interdisciplinary Sciences: Computational Life Sciences最新文献_第9页

DP-ID: Interleaving and Denoising to Improve the Quality of DNA Storage Image. DP-ID：交错和去噪提高 DNA 存储图像的质量。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2024-11-22 DOI: 10.1007/s12539-024-00671-6

Qi Xu, Yitong Ma, Zuhong Lu, Kun Bi

{"title":"DP-ID: Interleaving and Denoising to Improve the Quality of DNA Storage Image.","authors":"Qi Xu, Yitong Ma, Zuhong Lu, Kun Bi","doi":"10.1007/s12539-024-00671-6","DOIUrl":"10.1007/s12539-024-00671-6","url":null,"abstract":"In the field of storing images into DNA, the code tables and universal error correction codes have the potential to mitigate the effect of base errors to a certain extent. However, they prove to be ineffective in dealing with indels (insertion and deletion errors), resulting in a decline in information density and the quality of reconstructed image. This paper proposes a novel encoding and decoding method named DP-ID for storing images into DNA that improves information density and the quality of reconstructed image. Firstly, the image is compressed as bitstreams by the dynamic programming algorithm. Secondly, the bitstreams obtained are mapped to DNA, which are then interleaved. The reconstructed image is obtained by applying median filtering to remove salt-and-pepper noise. Simulation results show the reconstructed image by DP-ID at 5% error rate is better than that by other methods at 1% error rate. This robustness to high errors is compatible with the unsatisfied biological constraints caused by high information density. Wet experiments show that DP-ID can reconstruct high quality image at 5X sequencing depth. The high information density and low sequencing depth significantly reduce the cost of DNA storage, facilitating the large-scale storage of images into DNA.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"306-320"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692977","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 Multi-View Feature-Based Interpretable Deep Learning Framework for Drug-Drug Interaction Prediction. 基于多视图特征的药物-药物相互作用预测可解释深度学习框架。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2025-02-03 DOI: 10.1007/s12539-025-00687-6

Zihui Cheng, Zhaojing Wang, Xianfang Tang, Xinrong Hu, Fei Yang, Xiaoyun Yan

{"title":"A Multi-View Feature-Based Interpretable Deep Learning Framework for Drug-Drug Interaction Prediction.","authors":"Zihui Cheng, Zhaojing Wang, Xianfang Tang, Xinrong Hu, Fei Yang, Xiaoyun Yan","doi":"10.1007/s12539-025-00687-6","DOIUrl":"10.1007/s12539-025-00687-6","url":null,"abstract":"Drug-drug interactions (DDIs) can result in deleterious consequences when patients take multiple medications simultaneously, emphasizing the critical need for accurate DDI prediction. Computational methods for DDI prediction have garnered recent attention. However, current approaches concentrate solely on single-view features, such as atomic-view or substructure-view features, limiting predictive capacity. The scarcity of research on interpretability studies based on multi-view features is crucial for tracing interactions. Addressing this gap, we present MI-DDI, a multi-view feature-based interpretable deep learning framework for DDI. To fully extract multi-view features, we employ a Message Passing Neural Network (MPNN) to learn atomic features from molecular graphs generated by RDkit, and transformer encoders are used to learn substructure-view embeddings from drug SMILES simultaneously. These atomic-view and substructure-view features are then amalgamated into a holistic drug embedding matrix. Subsequently, an intricately designed interaction module not only establishes a tractable path for understanding interactions but also directly informs the construction of weight matrices, enabling precise and interpretable interaction predictions. Validation on the BIOSNAP dataset and DrugBank dataset demonstrates MI-DDI's superiority. It surpasses the current benchmarks by a substantial average of 3% on BIOSNAP and 1% on DrugBank. Additional experiments underscore the significance of atomic-view information for DDI prediction and confirm that our interaction module indeed learns more effective information for DDI prediction. The source codes are available at https://github.com/ZihuiCheng/MI-DDI .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"437-448"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143079679","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

MVGNCDA: Identifying Potential circRNA-Disease Associations Based on Multi-view Graph Convolutional Networks and Network Embeddings. MVGNCDA：基于多视图图卷积网络和网络嵌入识别潜在的circrna -疾病关联。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2025-02-17 DOI: 10.1007/s12539-025-00690-x

Guicong Sun, Mengxin Zheng, Yongxian Fan, Xiaoyong Pan

{"title":"MVGNCDA: Identifying Potential circRNA-Disease Associations Based on Multi-view Graph Convolutional Networks and Network Embeddings.","authors":"Guicong Sun, Mengxin Zheng, Yongxian Fan, Xiaoyong Pan","doi":"10.1007/s12539-025-00690-x","DOIUrl":"10.1007/s12539-025-00690-x","url":null,"abstract":"Increasing evidences have indicated that circular RNAs play a crucial role in the onset and progression of various diseases. However, exploring potential disease-associated circRNAs using conventional experimental techniques remains both time-intensive and costly. Recently, various computational approaches have been developed to detect the circRNA-disease associations. Nevertheless, due to the sparsity of the data and the inefficient utilization of similarity representation, it is still a challenge to effectively detect unknown circRNA-disease associations using multisource data. In this work, we propose an innovative computational framework, MVGNCDA, which merges a multi-view graph convolutional network (GCN) and biased random walk-based network embeddings to evaluate potential circRNA-disease associations from multisource data. First, we calculate disease semantic similarity, circRNA functional similarity, and their Gaussian interaction profile (GIP) kernel and cosine similarity. MVGNCDA utilizes multi-view GCNs to extract local node embeddings of diseases and circRNAs in the context of multisource information. Then, we construct a heterogeneous network utilizing integrated similarity and verified circRNA-disease associations, which is subsequently used to learn global node embeddings. Furthermore, the final fused local and global node embeddings are decoded to evaluate the circRNA-disease associations using a bilinear decoder. The fivefold cross-validation results demonstrate that MVGNCDA outperforms existing methods across five public datasets. Moreover, case study also confirms that MVGNCDA is capable of efficiently identifying unknown circRNA-disease associations.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"449-462"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440761","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

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation. CEL：通过弹性权重整合利用领域适应的疾病爆发预测的持续学习模型。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2025-02-28 DOI: 10.1007/s12539-024-00675-2

Saba Aslam, Abdur Rasool, Xiaoli Li, Hongyan Wu

{"title":"CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation.","authors":"Saba Aslam, Abdur Rasool, Xiaoli Li, Hongyan Wu","doi":"10.1007/s12539-024-00675-2","DOIUrl":"10.1007/s12539-024-00675-2","url":null,"abstract":"Continual learning is the ability of a model to learn over time without forgetting previous knowledge. Therefore, adapting new data in dynamic fields like disease outbreak prediction is paramount. Deep neural networks are prone to error due to catastrophic forgetting. This study introduces a novel CEL model for Continual Learning by leveraging domain adaptation via Elastic weight consolidation (EWC). This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting. The Fisher information matrix (FIM) is constructed with EWC to develop a regularization term that penalizes changes to essential parameters. We conducted experiments on three distinct diseases, influenza, mpox, and measles, with customized metrics. The high R-squared values during evaluation and reevaluation outperform the other state-of-the-art models in several contexts. The results indicate that CEL adapts well to incremental data. CEL's robustness emphasizes its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights CEL's versatility in disease outbreak prediction by addressing evolving data with temporal patterns. It offers a valuable model for proactive disease control with accurate and timely predictions.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"390-408"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143523276","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 : 2025-06-01 Epub 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":"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":"477-495"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","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

Plant lncRNA-miRNA Interaction Prediction Based on Counterfactual Heterogeneous Graph Attention Network. 基于反事实异质图注意网络的植物 lncRNA-miRNA 相互作用预测

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2024-10-09 DOI: 10.1007/s12539-024-00652-9

Yu He, ZiLan Ning, XingHui Zhu, YinQiong Zhang, ChunHai Liu, SiWei Jiang, ZheMing Yuan, HongYan Zhang

{"title":"Plant lncRNA-miRNA Interaction Prediction Based on Counterfactual Heterogeneous Graph Attention Network.","authors":"Yu He, ZiLan Ning, XingHui Zhu, YinQiong Zhang, ChunHai Liu, SiWei Jiang, ZheMing Yuan, HongYan Zhang","doi":"10.1007/s12539-024-00652-9","DOIUrl":"10.1007/s12539-024-00652-9","url":null,"abstract":"Identifying interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) provides a new perspective for understanding regulatory relationships in plant life processes. Recently, computational methods based on graph neural networks (GNNs) have been widely employed to predict lncRNA-miRNA interactions (LMIs), which compensate for the inadequacy of biological experiments. However, the low-semantic and noise of graph limit the performance of existing GNN-based methods. In this paper, we develop a novel Counterfactual Heterogeneous Graph Attention Network (CFHAN) to improve the robustness to against the noise and the prediction of plant LMIs. Firstly, we construct a real-world based lncRNA-miRNA (L-M) heterogeneous network. Secondly, CFHAN utilizes the node-level attention, the semantic-level attention, and the counterfactual links to enhance the node embeddings learning. Finally, these embeddings are used as inputs for Multilayer Perceptron (MLP) to predict the interactions between lncRNAs and miRNAs. Evaluating our method on a benchmark dataset of plant LMIs, CFHAN outperforms five state-of-the-art methods, and achieves an average AUC and average ACC of 0.9953 and 0.9733, respectively. This demonstrates CFHAN's ability to predict plant LMIs and exhibits promising cross-species prediction ability, offering valuable insights for experimental LMI researches.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"244-256"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390340","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

BiGM-lncLoc: Bi-level Multi-Graph Meta-Learning for Predicting Cell-Specific Long Noncoding RNAs Subcellular Localization. BiGM-lncLoc：预测细胞特异性长链非编码rna亚细胞定位的双水平多图元学习。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2024-12-26 DOI: 10.1007/s12539-024-00679-y

Xi Deng, Lin Liu

{"title":"BiGM-lncLoc: Bi-level Multi-Graph Meta-Learning for Predicting Cell-Specific Long Noncoding RNAs Subcellular Localization.","authors":"Xi Deng, Lin Liu","doi":"10.1007/s12539-024-00679-y","DOIUrl":"10.1007/s12539-024-00679-y","url":null,"abstract":"The precise spatiotemporal expression of long noncoding RNAs (lncRNAs) plays a pivotal role in biological regulation, and aberrant expression of lncRNAs in different subcellular localizations has been intricately linked to the onset and progression of a variety of cancers. Computational methods provide effective means for predicting lncRNA subcellular localization, but current studies either ignore cell line and tissue specificity or the correlation and shared information among cell lines. In this study, we propose a novel approach, BiGM-lncLoc, treating the prediction of lncRNA subcellular localization across cell lines as a multi-graph meta-learning task. Our investigation involves two categories of data: the localization data of nucleotide sequences in different cell lines and cell line expression data. BiGM-lncLoc comprises a cell line-specific optimization network learning specific knowledge from cell line expression data and a graph neural network optimized across cell lines. Subsequently, the specific and shared knowledge acquired through bi-level optimization is applied to a new cell-line prediction task without the need for re-training or fine-tuning. Additionally, through key feature analysis of the impact of different nucleotide combinations on the model, we confirm the necessity of cell line-specific studies based on correlation analysis. Finally, experiments conducted on various cell lines with different data sizes indicate that BiGM-lncLoc outperforms other methods in terms of prediction accuracy, with an average accuracy of 97.7%. After removing overlapping samples to ensure data independence for each cell line, the accuracy ranged from 82.4% to 94.7%, still surpassing existing models. Our code can be found at https://github.com/BioCL1/BiGM-lncLoc .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"359-374"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894249","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

NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields. 基于残差图卷积网络和条件随机场的微生物-药物关联预测。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2025-01-07 DOI: 10.1007/s12539-024-00678-z

Xiaoxin Du, Jingwei Li, Bo Wang, Jianfei Zhang, Tongxuan Wang, Junqi Wang

{"title":"NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields.","authors":"Xiaoxin Du, Jingwei Li, Bo Wang, Jianfei Zhang, Tongxuan Wang, Junqi Wang","doi":"10.1007/s12539-024-00678-z","DOIUrl":"10.1007/s12539-024-00678-z","url":null,"abstract":"The process of discovering new drugs related to microbes through traditional biological methods is lengthy and costly. In response to these issues, a new computational model (NRGCNMDA) is proposed to predict microbe-drug associations. First, Node2vec is used to extract potential associations between microorganisms and drugs, and a heterogeneous network of microbes and drugs is constructed. Then, a Graph Convolutional Network incorporating a fusion residual network mechanism (REGCN) is utilized to learn meaningful high-order similarity features. In addition, conditional random fields (CRF) are applied to ensure that microbes and drugs have similar feature embeddings. Finally, unobserved microbe-drug associations are scored based on combined embeddings. The experimental findings demonstrate that the NRGCNMDA approach outperforms several existing deep learning methods, and its AUC and AUPR values are 95.16% and 93.02%, respectively. The case study demonstrates that NRGCNMDA accurately predicts drugs associated with Enterococcus faecalis and Listeria monocytogenes, as well as microbes associated with ibuprofen and tetracycline.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"344-358"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142948365","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

Identification of Multi-functional Therapeutic Peptides Based on Prototypical Supervised Contrastive Learning. 基于原型监督对比学习的多功能治疗肽识别。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2024-12-23 DOI: 10.1007/s12539-024-00674-3

Sitong Niu, Henghui Fan, Fei Wang, Xiaomei Yang, Junfeng Xia

{"title":"Identification of Multi-functional Therapeutic Peptides Based on Prototypical Supervised Contrastive Learning.","authors":"Sitong Niu, Henghui Fan, Fei Wang, Xiaomei Yang, Junfeng Xia","doi":"10.1007/s12539-024-00674-3","DOIUrl":"10.1007/s12539-024-00674-3","url":null,"abstract":"High-throughput sequencing has exponentially increased peptide sequences, necessitating a computational method to identify multi-functional therapeutic peptides (MFTP) from their sequences. However, existing computational methods are challenged by class imbalance, particularly in learning effective sequence representations. To address this, we propose PSCFA, a prototypical supervised contrastive learning with a feature augmentation method for MFTP prediction. We employ a two-stage training scheme to train the feature extractor and the classifier respectively, underpinned by the principle that better feature representation boosts classification accuracy. In the first stage, we utilize a prototypical supervised contrastive learning strategy to enhance the uniformity of feature space distribution, ensuring that the characteristics of samples within the same category are tightly clustered while those from different categories are more dispersed. In the second stage, a feature augmentation strategy that focuses on infrequent labels (tail labels) is used to refine the learning process of the classifier. We use a prototype-based variational autoencoder to capture semantic links among common labels (head labels) and their prototypes. This knowledge is then transferred to tail labels, generating enhanced features for classifier training. The experiments prove that the PSCFA method significantly outperforms existing methods for MFTP prediction, making a significant advancement in therapeutic peptide identification.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"332-343"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876975","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

CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation. CR-deal: circRNA-RBP结合位点识别和解释的可解释神经网络。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-06-01 Epub Date: 2025-03-27 DOI: 10.1007/s12539-025-00694-7

Yuxiao Wei, Zhebin Tan, Liwei Liu

{"title":"CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation.","authors":"Yuxiao Wei, Zhebin Tan, Liwei Liu","doi":"10.1007/s12539-025-00694-7","DOIUrl":"10.1007/s12539-025-00694-7","url":null,"abstract":"circRNAs are a type of single-stranded non-coding RNA molecules, and their unique feature is their closed circular structure. The interaction between circRNAs and RNA-binding proteins (RBPs) plays a key role in biological functions and is crucial for studying post-transcriptional regulatory mechanisms. The genome-wide circRNA binding event data obtained by cross-linking immunoprecipitation sequencing technology provides a foundation for constructing efficient computational model prediction methods. However, in existing studies, although machine learning techniques have been applied to predict circRNA-RBP interaction sites, these methods still have room for improvement in accuracy and lack interpretability. We propose CR-deal, which is an interpretable joint deep learning network that predicts the binding sites of circRNA and RBP through genome-wide circRNA data. CR-deal utilizes a graph attention network to unify sequence and structural features into the same view, more effectively utilizing structural features to improve accuracy. It can infer marker genes in the binding site through integrated gradient feature interpretation, thereby inferring functional structural regions in the binding site. We conducted benchmark tests on CR-deal on 37 circRNA datasets and 7 lncRNA datasets, respectively, and obtained the interpretability of CR-deal and discovered functional structural regions through 5 circRNA datasets. We believe that CR-deal can help researchers gain a deeper understanding of the functions and mechanisms of circRNA in living organisms and its critical role in the occurrence and development of diseases. The source code of CR-deal is provided free of charge on https://github.com/liuliwei1980/CR .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"463-476"},"PeriodicalIF":3.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718686","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