SSCRB: Predicting circRNA-RBP Interaction Sites Using a Sequence and Structural Feature-Based Attention Model

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2024, Volume and Issue: 28(3), P. 1762 - 1772

Published: Jan. 15, 2024

The prediction of interaction sites between circular RNA (circRNA) and binding proteins (RBPs) is crucial for regulating diseases discovering new treatment approaches. Computational models have been widely used to predict circRNA-RBP due the availability genome-wide circRNA event data. However, efficiently obtaining multi-scale features improve accuracy remains a challenging problem. In this study, we propose SSCRB, lightweight model predicting sites. Our extracts both sequence structural incorporates through attention mechanism. Furthermore, develop an ensemble by combining multiple submodels enhance predictive performance generalizability. We evaluate SSCRB on 37 datasets compare it with other state-of-the-art methods. average AUC 97.66%, demonstrating its efficiency robustness. outperforms methods in terms while requiring significantly fewer computational resources.

Language: Английский

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Role of circular RNAs in cancer therapy resistance

Molecular Cancer, Journal Year: 2025, Volume and Issue: 24(1)

Published: Feb. 25, 2025

Over the past decade, circular RNAs (circRNAs) have gained recognition as a novel class of genetic molecules, many which are implicated in cancer pathogenesis via different mechanisms, including drug resistance, immune escape, and radio-resistance. ExosomalcircRNAs, particular, facilitatecommunication between tumour cells micro-environmental cells, fibroblasts, other components. Notably, can reportedly influence progression treatment resistance by releasing exosomalcircRNAs. circRNAs often exhibit tissue- cancer-specific expression patterns, growing evidence highlights their potential clinical relevance utility. These molecules show strong promise biomarkers therapeutic targets for diagnosis treatment. Therefore, this review aimed to briefly discuss latest findings on roles mechanisms key various malignancies, lung, breast, liver, colorectal, gastric cancers, well haematological malignancies neuroblastoma.This will contribute identification new circRNA early cancer.

Language: Английский

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A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation

BMC Biology, Journal Year: 2024, Volume and Issue: 22(1)

Published: Jan. 29, 2024

Abstract Background Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring relationship between circRNAs diseases is far-reaching significance for studying etiopathogenesis treating To this end, based on graph Markov neural network algorithm (GMNN) constructed our previous work GMNN2CD, we further considered multisource biological data that affects association circRNA disease developed an updated web server CircDA human hepatocellular carcinoma (HCC) tissue verify prediction results CircDA. Results built Tumarkov-based deep learning framework. The regards biomolecules as nodes interactions molecules edges, reasonably abstracts multiomics data, models them heterogeneous biomolecular network, which can reflect complex different biomolecules. Case studies using literature from HCC, cervical, gastric cancers demonstrate predictor identify missing associations known diseases, quantitative real-time PCR (RT-qPCR) experiment HCC samples, it was found five were significantly differentially expressed, proved predict related new circRNAs. Conclusions This efficient computational case analysis with sufficient feedback allows us circRNA-associated disease-associated Our provides method provide guidance certain For ease use, online ( http://server.malab.cn/CircDA ) provided, code open-sourced https://github.com/nmt315320/CircDA.git convenience improvement.

Language: Английский

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Circular RNAs in the KRAS pathway: Emerging players in cancer progression

Pathology - Research and Practice, Journal Year: 2024, Volume and Issue: 256, P. 155259 - 155259

Published: March 11, 2024

Language: Английский

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Deep learning and ensemble deep learning for circRNA-RBP interaction prediction in the last decade: A review

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 123, P. 106352 - 106352

Published: May 2, 2023

Language: Английский

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AMDECDA: Attention Mechanism Combined With Data Ensemble Strategy for Predicting CircRNA-Disease Association

IEEE Transactions on Big Data, Journal Year: 2023, Volume and Issue: 10(4), P. 320 - 329

Published: Nov. 20, 2023

Accumulating evidence from recent research reveals that circRNA is tightly bound to human complex disease and plays an important regulatory role in progression. Identifying disease-associated occupies a key the of pathogenesis. In this study, we propose new model AMDECDA for predicting circRNA-disease association (CDA) by combining attention mechanism data ensemble strategy. Firstly, fuse heterogeneous information including Gaussian interaction profile (GIP), semantics GIP, then use Graph Attention Network (GAT) focus on critical data, reasonably allocate resources extract their essential features. Finally, deep RVFL network (edRVFL) utilized quickly accurately predict CDA non-iterative manner closed-form solutions. five-fold cross-validation experiment benchmark set, achieves accuracy 93.10% with sensitivity 97.56% 0.9235 AUC. comparison previous models, exhibits highly competitiveness. Furthermore, 26 top 30 unknown CDAs predicted scores are proved related literature. These results indicate can effectively anticipate latent provide help further biological wet experiments.

Language: Английский

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BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(16), P. 5384 - 5394

Published: Aug. 3, 2023

More and more evidence suggests that circRNA plays a vital role in generating treating diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA-miRNA interaction (CMI) has become urgent. However, traditional wet experiments are time-consuming costly, the results will be affected objective factors. In this paper, we propose computational model BCMCMI, which combines three features to predict CMI. Specifically, BCMCMI utilizes bidirectional encoding capability BERT algorithm extract sequence from semantic information Then, heterogeneous network is constructed based on cosine similarity known CMI information. The Metapath2vec employed conduct random walks following meta-paths capture topological features, including features. Finally, CMIs predicted using XGBoost classifier. achieves superior compared other state-of-the-art models two benchmark datasets for prediction. We also utilize t-SNE visually observe distribution extracted randomly selected dataset. remarkable show can serve as valuable complement experiment process.

Language: Английский

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iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction

PLoS Computational Biology, Journal Year: 2023, Volume and Issue: 19(8), P. e1011344 - e1011344

Published: Aug. 31, 2023

Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful understanding pathogenesis, diagnosis, and prevention, as well identifying relevant biomarkers. During the past few years, a large number of deep learning (DL) based methods have been proposed for predicting circRNA-disease achieved impressive performance. However, there are two main drawbacks to these methods. The first underutilize biometric information data. Second, features extracted by not outstanding represent characteristics between In this study, we developed novel model, named iCircDA-NEAE, predict associations. particular, use disease semantic similarity, Gaussian interaction profile kernel, circRNA expression Jaccard similarity simultaneously time, extract hidden on accelerated attribute network embedding (AANE) dynamic convolutional autoencoder (DCAE). Experimental results circR2Disease dataset show iCircDA-NEAE outperforms other competing significantly. Besides, 16 top 20 pairs with highest scores were validated literature. Furthermore, observe can effectively new potential

Language: Английский

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CircSI-SSL: circRNA-binding site identification based on self-supervised learning

Bioinformatics, Journal Year: 2024, Volume and Issue: 40(1)

Published: Jan. 1, 2024

Abstract Motivation In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading development numerous protein site identification algorithms. Unfortunately, these studies are supervised and require vast majority labeled samples in training produce superior performance. But acquisition sample labels requires large number biological experiments is difficult obtain. Results To resolve this matter that great deal tags need be trained circRNA-binding prediction task, self-supervised learning binding algorithm named CircSI-SSL proposed article. According survey, unprecedented research field. Specifically, initially combines multiple feature coding schemes employs RNA_Transformer for cross-view sequence (self-supervised task) learn mutual information from multi-view data, then fine-tuning only few labels. Comprehensive on six widely used circRNA datasets indicate our achieves excellent performance comparison previous algorithms, even extreme case where ratio data test 1:9. addition, transplantation experiment linRNA without network modification hyperparameter adjustment shows has good scalability. summary, based article expected replace algorithms more extensive application value. Availability implementation The source code available at https://github.com/cc646201081/CircSI-SSL.

Language: Английский

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GEnDDn: An lncRNA–Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network

Interdisciplinary Sciences Computational Life Sciences, Journal Year: 2024, Volume and Issue: 16(2), P. 418 - 438

Published: May 11, 2024

Language: Английский

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