Interdisciplinary Sciences: Computational Life Sciences最新文献

{"title":"A Deep Learning Framework for Accurate lncRNA-Protein Interaction Prediction via Multi-Kernel Graph Embeddings and Self-Attention.","authors":"Shivani Saxena, Ahsan Z Rizvi","doi":"10.1007/s12539-026-00872-1","DOIUrl":"https://doi.org/10.1007/s12539-026-00872-1","url":null,"abstract":"<p><p>Long non-coding RNAs (lncRNAs) play a pivotal role in regulating biological processes, primarily through lncRNA-Protein Interactions (LPI), which are crucial for understanding disease mechanisms and cellular functions. While computational methods exist for predicting LPI, they suffer from suboptimal feature integration, limited generalizability, and inadequate network modeling. To address these challenges, we propose MAKGLPIN (Multi-Architecture, Multi-Kernel Graph-based LPI Network), a novel deep learning framework that integrates sequence features, structural information, and physicochemical properties of lncRNAs and proteins. MAKGLPIN employs a multi-layer Graph Convolutional Network (GCN) enhanced with a self-attention mechanism to capture complex interaction patterns, alongside a Graph Attention Network (GAT) to prioritize critical interactions while preserving long-range dependencies. Extensive evaluations demonstrate that MAKGLPIN outperforms existing methods in robustness, generalizability, and predictive precision across benchmark datasets. Our model provides a computationally efficient and biologically interpretable solution, advancing the study of LPI and their applications in disease research.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207013","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":"scDGCN: A Graph-Based Deep Clustering Framework for Single-Cell Transcriptomics via Neighborhood-Enhanced Context Representation Learning.","authors":"Binhua Tang, Yingying Feng, Guowei Cheng, Jingyu Wang","doi":"10.1007/s12539-026-00884-x","DOIUrl":"https://doi.org/10.1007/s12539-026-00884-x","url":null,"abstract":"<p><p>Self-supervised and context-aware representation learning offers a powerful strategy for extracting robust features from high-dimensional, sparse, and noisy single-cell data. Here, we present scDGCN, a graph-based deep clustering framework for single-cell RNA sequencing (scRNA-seq) analysis. scDGCN models intercellular similarity as a graph and leverages local neighborhood context to guide expression reconstruction, thereby learning robust cell representations. The method integrates a network enhancement module to denoise the cell similarity graph, a neighborhood-enhanced autoencoder that reconstructs gene expression using topological neighborhood context, and a graph neural network to preserve structural semantics among cells. By embedding each cell's expression profile within its neighbors, scDGCN captures subtle transcriptional variation and identifies cell subtypes without labeled references. Across ten scRNA-seq datasets from diverse human and mouse tissues, scDGCN achieves competitive adjusted Rand index (ARI), normalized mutual information (NMI), and clustering accuracy (ACC), supporting unsupervised identification of cellular subpopulations. These results demonstrate that combining graph denoising, neighborhood-enhanced context reconstruction, and graph-structured representation learning improves unsupervised single-cell clustering and facilitates high-resolution cell atlas construction.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149181567","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":"LLM4PDA: Augmenting piRNA-Disease Association Prediction with Large Language Models.","authors":"Dengju Yao, Xiangkui Li, Xiaojuan Zhan, Bo Zhang, Jian Zhang","doi":"10.1007/s12539-026-00874-z","DOIUrl":"https://doi.org/10.1007/s12539-026-00874-z","url":null,"abstract":"<p><p>PIWI-interacting RNAs (piRNAs) maintain genomic stability by interacting with PIWI proteins and have been implicated in various human diseases. However, wet-lab experiments remain limited in throughput, creating an urgent need for computational methods to systematically identify piRNA-disease associations. Although numerous computational approaches have been proposed, they still face major challenges such as limited feature representations and insufficient integration of biological knowledge. This study presents LLM4PDA, a large language model-augmented framework for piRNA-disease association prediction. PiRNA sequences are encoded using RNA-FM, whereas disease representations are derived from LLM-generated disease descriptions guided by a reusable, structured prompt template and encoded using OpenAI's text-embedding models. These representations are mapped into a common latent space through an MLP-based alignment module, enabling association scoring for candidate piRNA-disease pairs. Separately from the predictive model, a post-prediction interpretive module organizes high-confidence predictions into structured, knowledge-informed biological summaries to support hypothesis generation and guide downstream experimental validation. Five-fold cross-validation demonstrates that LLM4PDA achieves area under the curve (AUC) values of 0.984 and 0.985 on two benchmark datasets, outperforming eight state-of-the-art methods. Case studies further illustrate how the interpretive module facilitates structured synthesis of biological context for selected high-confidence associations.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149181587","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":"Musical Peptide: Fragmentation of Musical Protein for AI-driven Bioactive Peptide Discovery Based on a New DeepPepQSAR Pipeline.","authors":"Peng Zhou, Jun Su, Kexin Li, Yulu Gan, Yunyi Zhang","doi":"10.1007/s12539-026-00882-z","DOIUrl":"https://doi.org/10.1007/s12539-026-00882-z","url":null,"abstract":"<p><p>Musical protein (MPro), the protein defined by music (namely biologization of music), is a reverse operation of the traditional life music that addresses the sonification of biomolecule to musical audio. We previously described a computational strategy for MPro facilitation, which maps the time sequence of music onto the spatial architecture of protein. Here, we further extend MPro to musical peptide (MPep) by fragmentizing MPro protein at either music time sequence level or protein residue sequence level, which represents a new technique to de novo generate distinct peptidic big data with intra-sequence organization, thus particularly suitable for AI-oriented tasks. On the other side, DeepPepQSAR methodology is raised as an integration of deep learning and traditional peptide quantitative structure-activity relationship (PepQSAR) in big-data bioactive peptide discovery, and we herein practice it on AI-driven high-throughput antimicrobial peptide (AMP) screening. Both the structural properties of length-varying peptide sequences and the bacterial species tested for these peptides are visually characterized by using PepImage representation, which is then fed into a dual-path convolutional neural network (CNN) to train a predictive DeepPepQSAR regression scoring function for further discovering potent AMPs from millions of MPep candidates. Consequently, 32 MPep peptides are identified to have a potential antibacterial profile, in which 4 and 12 peptides exhibit high and moderate activities, respectively, that are generally comparable with the sophisticated AMP peptide KR-12, but lower than the FDA-approved antibiotic Levofloxacin.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149181572","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":"MDCL: Multi-source Information Fusion and Collaborative Contrastive Learning for Inferring miRNA-Drug Sensitivity and Resistance Associations.","authors":"Lan Huang, Chenxu Si, Yunzhi Liu, Ziyun An, Jiana Ding, Zhiyao Han, Nan Sheng, Yan Wang","doi":"10.1007/s12539-026-00879-8","DOIUrl":"https://doi.org/10.1007/s12539-026-00879-8","url":null,"abstract":"<p><p>A growing body of evidence indicates that microRNAs (miRNAs) play essential roles in cancer progression, therapy, and drug resistance. They are increasingly recognized as promising therapeutic targets for overcoming cancer drug resistance. However, existing computational methods for predicting miRNA-drug associations are often limited by sparse labeled associations, reliance on single-modality data, and insufficient ability to capture long-range dependencies in sequences. In this study, we propose a novel deep learning framework, named MDCL, that integrates multi-source information fusion with collaborative contrastive learning. MDCL uses a multi-scale convolutional neural network (multi-scale CNN) to learn sequence embeddings at different granularities from miRNA sequences and drug SMILES strings. In parallel, frequency chaos game representation (FCGR) and multiple molecular fingerprints are used to characterize the two-dimensional structural patterns of miRNAs and the physicochemical properties of drugs, respectively. To effectively integrate multi-view information, the model introduces a cross-view attention mechanism for feature enhancement and constructs a local-global alignment loss within a collaborative contrastive learning (CCL) framework, substantially improving feature robustness and discriminability. We evaluated MDCL on benchmark datasets manually curated from the latest database release. Extensive five-fold cross-validation experiments, repeated five times with different random seeds, show that MDCL consistently outperforms state-of-the-art methods, achieving an AUC of 0.9450 and an AUPR of 0.9281 for miRNA-drug resistance (MDR) association prediction, and an AUC of 0.9379 and an AUPR of 0.9293 for miRNA-drug sensitivity (MDS) association prediction. MDCL also attains the best F1-score and Matthews correlation coefficient (MCC) among all compared methods. Moreover, under a strict independent-node cold-start setting, in which both miRNAs and drugs in the test set are unseen during training, MDCL remains competitive in ranking-based metrics, demonstrating strong generalization ability. In addition, a case study of docetaxel further demonstrates the practical utility of the model for discovering novel miRNA-drug associations. The data and source code are available at https://github.com/scxdhr020629/MDCL .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149166427","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":"SpliceDetector: A Splice Sites Detection Method Based on Biology Attention and Ensemble Module.","authors":"Jiquan Shen, Yingying Wang, Junwei Luo","doi":"10.1007/s12539-026-00878-9","DOIUrl":"https://doi.org/10.1007/s12539-026-00878-9","url":null,"abstract":"<p><p>Splice site detection is a fundamental task in genome annotation, yet existing deep learning methods still have limitations in incorporating biological prior knowledge and extracting multi-scale features. This study presents SpliceDetector, a multi-scale residual attention network based on multi-level attention mechanisms and ensemble strategies. SpliceDetector employs a Biology Attention mechanism that utilizes tri-scale depthwise separable convolutions to capture short-range splice site motifs GT-AG, medium-range regulatory elements (ESE/ESS), and long-range sequence context (ISE/ISS). Three complementary sub-models further leverage ECA (Efficient Channel Attention), Biology Attention, and CBAM (Convolutional Block Attention Module) to improve feature representation from the channel, biological, and spatial dimensions, respectively. Their outputs are combined through validation-performance-based a weighted ensemble to improve prediction stability. SpliceDetector outperforms the compared methods on all six benchmark datasets from three species (O. sativa japonica, A. thaliana, and H. sapiens) for both donor-site and acceptor-site detection. Interpretability analyses further indicate that the learned attention distributions correspond to known splicing signals, suggesting that SpliceDetector captures biologically relevant sequence patterns rather than relying only on classification accuracy. The code implementation of SpliceDetector is publicly available at https://github.com/HpuBioinformatics/SpliceDetector . Article type: Original Article. SpliceDetector: A Splice Sites Detection Method Based on Biology Attention and Ensemble Module.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149147914","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":"scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.","authors":"Jiahui Yan, Mengyuan Zhao, Yanlin Jiang, Jijun Tang, Fei Guo","doi":"10.1007/s12539-026-00881-0","DOIUrl":"https://doi.org/10.1007/s12539-026-00881-0","url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic analysis at single-cell resolution, revealing cell identities and underlying gene regulatory mechanisms. This technology provides critical insights into cellular differentiation and disease mechanisms. However, due to technical limitations, scRNA-seq data suffer from dropout events that may frustrate the analyses. Existing imputation methods mainly rely on scRNA-seq data without effectively integrating additional biological information, and still have limited ability to model complex data structures, making it difficult to capture intrinsic structure. To solve these limitations, we propose scCADI, a conditional diffusion-based imputation framework for scRNA-seq data. This framework aims to achieve robust imputation across multiple datasets and application scenarios while facilitating downstream analyses. In scCADI, an autoencoder first projects each cell into the latent space, where the observed data and metadata are integrated as conditional priors to guide the diffusion process in learning biologically meaningful expression patterns. With this design, scCADI achieves accurate gene expression recovery while preserving intrinsic biological variability among cells. We evaluated the imputation performance on multiple datasets, results show that scCADI outperforms other competing methods across various analytical tasks, including gene expression recovery, cell clustering, cellular trajectory inference and differential expression analysis.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148479","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":"AntiLipo: A Comprehensive Database, Deep Learning-Based Prediction Model, and Computational Study of Anti-Hyperlipidemic Peptides.","authors":"Xueyan Duan, Yi He, Hanwen Liu, Hongyan Yan, Weiwei Han","doi":"10.1007/s12539-026-00876-x","DOIUrl":"https://doi.org/10.1007/s12539-026-00876-x","url":null,"abstract":"<p><p>Anti-hyperlipidemic peptides (ALPs) play important roles in regulating lipid metabolism and preventing atherosclerosis, yet conventional experimental identification methods remain time-consuming. In this study, we curated 201 experimentally validated ALPs with their sequences, sources, activity data, and metabolic pathways. Network pharmacology analysis revealed that ALPs primarily participate in canonical lipid-regulatory pathways, including AMPK and PPAR signaling, fatty acid metabolism, and cholesterol metabolism, as well as adipocytokine signaling and insulin resistance-related pathways. Based on this dataset, we developed five classification models (CNN, ESM-transformer, MLP, PhyChem-transformer, and Transformer). The transformer model achieved the best overall performance (Accuracy = 0.70, MCC = 0.40), while the MLP model attained the highest AUROC (0.76). For peptide-protein bioactivity prediction, five regression models were evaluated, with the self-attention model achieving the best performance (R² = 0.61, Pearson = 0.79). Furthermore, 500 ns molecular dynamics simulations were performed for 141 peptides against seven key lipid metabolism-related protein targets, yielding 161 active peptide-target trajectories freely available for download. Finally, we constructed AntiLipo web server ( https://hwwlab.com/webserver/antilipo ), the first specialized online database for ALPs, integrating modules for peptide structure, source, activity, mechanism, and predictive model deployment.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108976","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":"3D UXFormer: Dual-Branch Feature Fusion for Precise Breast MRI Multi-Tissue Segmentation.","authors":"Xiaodong Zheng, Zhitao Wei, Siyao Du, Chinting Wong, Yanting Liang, Yanfen Cui, Jinrong Qu, Jianlin Wu, Lina Zhang, Chu Han, Zaiyi Liu, Ying Wang, Peng Xu, Zhenwei Shi, Wenbin Liu","doi":"10.1007/s12539-026-00864-1","DOIUrl":"https://doi.org/10.1007/s12539-026-00864-1","url":null,"abstract":"<p><p>Accurate segmentation of breast tissues in MRI is essential for precise breast cancer diagnosis and effective treatment planning. Manually segmenting these tissues is not only arduous but also time-consuming. Fusing multi-phase MRI images, like the Contrast-enhanced Early ([Formula: see text]) and Contrast-enhanced Peak ([Formula: see text]) phases, to fully utilize their complementary information remains a challenge. In this paper, we introduce a novel 3D UXFormer model. It combines the 3D UNET and Vision Transformer (ViT) through a dual-branch structure, capitalizing on the 3D UNET's local feature extraction prowess and ViT's global feature-capturing ability. To address data-related issues, we pre-train ViT using the Masked Autoencoder (MAE) approach. Extensive experiments demonstrate that the 3D UXFormer outperforms state-of-the-art algorithms, achieving higher Dice and IoU scores across diverse breast tissues in different datasets. The source code of our method is available on Github.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149074519","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":"DiM2-DRP: Dual-Input Multi-View Dynamic Contrastive Learning Framework for Drug Response Prediction.","authors":"Shuang Du, Wenying Li, Zhennuo Wang, Shaoqiang Wang, Mingjian Jiang, Yuanyuan Zhang","doi":"10.1007/s12539-026-00880-1","DOIUrl":"https://doi.org/10.1007/s12539-026-00880-1","url":null,"abstract":"<p><p>Accurately modeling cellular states is crucial for pharmacogenomics, yet existing deep learning methods often rely on shallow representations that fail to capture complex drug-cell interactions. These limitations necessitate a multi-view architecture that synergistically leverages multiple biological modalities to explicitly capture drug-cell interactions. Here, we propose DiM2-DRP, a dual-input multi-view framework that integrates heterogeneous and complementary views from cancer cells and small-molecule compounds. The framework develops a network-informed cell encoder that integrates bi-directional network diffusion with stability-guided pathway clustering to derive compact, biologically consistent representations. A novel bi-directional attention-based contrastive fusion module integrates these cellular representations with drug features extracted by a structure-informed drug encoder, dynamically adjusting cross-modal alignment according to interaction strength. By emphasizing biologically relevant drug-cell dependencies, this architecture alleviates the shallow integration limitations of conventional methods. Furthermore, an auxiliary classification module imposes lineage-specific and target-pathway constraints, ensuring the learned representations are both mechanistically grounded and robust for drug response prediction. Experiments show that DiM2-DRP achieves higher predictive accuracy, improved drug-blind and cell-blind generalization, and enhanced biological interpretability compared with existing methods. The pathway signatures recovered by the model remain consistent in patient tumours and are associated with multiple clinical outcomes, supporting the translational relevance of the learned representations. By leveraging a multimodal architecture integrated with dynamic contrastive learning, DiM2-DRP refines the characterization of drug sensitivity, offering a robust framework to support precision oncology strategies.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149030295","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}