BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo

PLoS Computational Biology, Год журнала: 2023, Номер 19(6), С. e1011214 - e1011214

Опубликована: Июнь 20, 2023

As the key for biological sequence structure and function prediction, disease diagnosis treatment, similarity analysis has attracted more attentions. However, exiting computational methods failed to accurately analyse similarities because of various data types (DNA, RNA, protein, disease, etc) their low (remote homology). Therefore, new concepts techniques are desired solve this challenging problem. Biological sequences RNA protein sequences) can be considered as sentences "the book life", language semantics (BLS). In study, we seeking derived from natural processing (NLP) comprehensively similarities. 27 NLP were introduced similarities, bringing analysis. Experimental results show that these able facilitate development remote homology detection, circRNA-disease associations identification annotation, achieving better performance than other state-of-the-art predictors in related fields. Based on methods, a platform called BioSeq-Diabolo been constructed, which is named after popular traditional sport China. The users only need input embeddings data. will intelligently identify task, then based semantics. integrate different supervised manner by using Learning Rank (LTR), constructed evaluated analysed so recommend best users. web server stand-alone package accessed at http://bliulab.net/BioSeq-Diabolo/server/.

Язык: Английский

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Predicting circRNA–Disease Associations through Multisource Domain-Aware Embeddings and Feature Projection Networks

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Янв. 19, 2025

Recent studies have highlighted the significant role of circular RNAs (circRNAs) in various diseases. Accurately predicting circRNA–disease associations is crucial for understanding their biological functions and disease mechanisms. This work introduces MNDCDA method, designed to address challenges posed by limited number known high cost experiments. integrates multiple data sources with neighborhood-aware embedding models deep feature projection networks predict potential pathways linking circRNAs Initially, comprehensive biometric are used construct four similarity networks, forming a diverse interaction framework. Next, model captures structural information about diseases, while learn high-order interactions nonlinear connections. Finally, bilinear decoder identifies novel between The achieved an AUC 0.9070 on constructed benchmark dataset. In case studies, 25 out 30 predicted pairs were validated through wet lab experiments published literature. These extensive experimental results demonstrate that robust computational tool associations, providing valuable insights helping reduce research costs.

Язык: Английский

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Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 27(6), С. 3072 - 3082

Опубликована: Март 23, 2023

Exploring the relationship between circular RNA (circRNA) and disease is beneficial for revealing mechanisms of pathogenesis. However, a blind search all possible associations circRNAs diseases through biological experiments time-consuming. Although some prediction methods have been proposed, they still limitations. In this study, novel computational framework, called GATCL2CD, proposed to forecast unknown circRNA-disease (CDAs). First, we calculate Gaussian interactive profile kernel (GIP) similarity semantic diseases, circRNA sequence function similarity, GIPs circRNAs. Then, combine them construct heterogeneous graph. Thereafter, GATCL2CD proposes feature convolution learning that uses multi-head dynamic attention mechanism obtain different aggregated representations features correspond nodes in it extracts rich higher-order from stacked each node by using single-layer convolutional neural network with filter kernels sizes. Finally, pairwise element-wise product operation implemented capture interactions representations, multilayer perceptron introduced as an efficient classifier inferring potential CDAs. Major experimental results under 5-fold cross-validation (5-fold CV) on three datasets show superior five other state-of-the-art methods. Furthermore, case studies demonstrate suitability useful tool identifying disease-related

Язык: Английский

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MPCLCDA: predicting circRNA–disease associations by using automatically selected meta-path and contrastive learning

Briefings in Bioinformatics, Год журнала: 2023, Номер 24(4)

Опубликована: Май 31, 2023

Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between diseases and circRNA can help in disease prevention, diagnosis treatment. Traditional methods are time consuming laborious. Meanwhile, computational models effectively predict potential circRNA-disease (CDAs), but restricted by limited data, resulting data high dimension imbalance. In this study, we propose a model based on automatically selected meta-path contrastive learning, called MPCLCDA model. First, constructs new heterogeneous network similarity, similarity known association, via obtains low-dimensional fusion features of nodes graph convolutional networks. Then, learning used to optimize further, obtain node that make distinction positive negative samples more evident. Finally, scores predicted through multilayer perceptron. The proposed method compared advanced four datasets. average area under receiver operating characteristic curve, precision-recall curve F1 score 5-fold cross-validation reached 0.9752, 0.9831 0.9745, respectively. Simultaneously, case studies further prove predictive ability application value method.

Язык: Английский

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AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding

PLoS Computational Biology, Год журнала: 2024, Номер 20(1), С. e1011851 - e1011851

Опубликована: Янв. 30, 2024

The unique expression patterns of circRNAs linked to the advancement and prognosis cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce cost treatment. Computational prediction circRNA-cancer drug-cancer relationships is crucial precise therapy. However, prior computational methods fail analyze interaction between circRNAs, drugs, at systematic level. It essential propose a method that uncover more information achieving cancer-centered multi-association prediction. In this paper, we present novel method, AutoEdge-CCP, unveil cancer-associated drugs. We abstract complex into multi-source heterogeneous network. network, each molecule represented by two types information, one intrinsic attribute molecular features, other link explicitly modeled autoGNN, which searches from both intra-layer inter-layer message passing neural significant performance on multi-scenario applications case studies establishes AutoEdge-CCP potent promising association tool.

Язык: 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, Год журнала: 2024, Номер 22(1)

Опубликована: Янв. 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.

Язык: Английский

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LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features

IEEE/ACM Transactions on Computational Biology and Bioinformatics, Год журнала: 2024, Номер 21(5), С. 1413 - 1422

Опубликована: Апрель 12, 2024

CircRNA has been shown to be involved in the occurrence of many diseases. Several computational frameworks have proposed identify circRNA-disease associations. Despite existing methods obtained considerable successes, these still require improved as their performance may degrade due sparsity data and problem memory overflow. We develop a novel framework called LGCDA predict associations by fusing local global features solve above mentioned problems. First, we construct closed subgraphs using k-hop subgraph label obtain rich graph pattern information. Then, are extracted neural network (GNN). In addition, fuse Gaussian interaction profile (GIP) kernel cosine similarity features. Finally, score is predicted multilayer perceptron (MLP) based on perform five- fold cross validation five datasets for model evaluation our surpasses other advanced methods. The code available at https://github.com/lanbiolab/LGCDA

Язык: Английский

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Identification of ferroptosis-related lncRNAs for predicting prognosis and immunotherapy response in non-small cell lung cancer

Future Generation Computer Systems, Год журнала: 2024, Номер 159, С. 204 - 220

Опубликована: Май 18, 2024

Язык: Английский

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Circular RNA-Drug Association Prediction Based on Multi-Scale Convolutional Neural Networks and Adversarial Autoencoders

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(4), С. 1509 - 1509

Опубликована: Фев. 11, 2025

The prediction of circular RNA (circRNA)-drug associations plays a crucial role in understanding disease mechanisms and identifying potential therapeutic targets. Traditional methods often struggle to cope with the complexity heterogeneous networks high dimensionality biological data. In this study, we propose circRNA-drug association method based on multi-scale convolutional neural (MSCNN) adversarial autoencoders, named AAECDA. First, construct feature network by integrating circRNA sequence similarity, drug structure known associations. Then, unlike conventional networks, employ MSCNN extract hierarchical features from integrated network. Subsequently, characteristics are introduced further refine these through an autoencoder, obtaining low-dimensional representations. Finally, learned representations fed into deep predict novel Experiments show that AAECDA outperforms various baseline predicting Additionally, case studies demonstrate our model is applicable practical related tasks.

Язык: Английский

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metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge Learning

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

In the emerging field of RNA drugs, circular (circRNA) has attracted much attention as a novel multifunctional therapeutic target. Delving deeper into intricate interactions between circRNA and disease is critical for driving drug discovery efforts centered around circRNAs. Current computational methods face two significant limitations: lack aggregate information in heterogeneous graph networks higher-order fusion information. To this end, we present approach, metaCDA, which utilizes meta-knowledge adaptive learning to improve accuracy association predictions addresses limitations both. We calculate multiple similarity measures circRNA, construct based on these, apply meta-networks extract from graph, so that constructed maps have contrast enhancement Then, nodal aggregation system, integrates multihead mechanism mechanism, achieve accurate capture conducted extensive experiments, results show metaCDA outperforms existing state-of-the-art models can effectively predict disease-associated opening up new prospects circRNA-driven discovery.

Язык: Английский

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