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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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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scMGATGRN: a multiview graph attention network–based method for inferring gene regulatory networks from single-cell transcriptomic data

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(6)

Опубликована: Сен. 23, 2024

Abstract The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex relationships, exploring disease mechanisms. Recently, deep learning (DL)–based methods have been proposed to infer GRNs from single-cell transcriptomic data achieved impressive performance. However, these do not fully utilize graph topological information high-order neighbor multiple receptive fields. To overcome those limitations, we propose novel model based on multiview attention network, namely, scMGATGRN, GRNs. scMGATGRN mainly consists GAT, multiview, view-level mechanism. GAT can extract essential features network. simultaneously local feature nodes mechanism dynamically adjusts relative importance node embedding representations efficiently aggregates two views. verify effectiveness compared its performance with 10 (five shallow algorithms five state-of-the-art DL-based methods) seven benchmark RNA sequencing (scRNA-seq) datasets cell lines (two human three mouse) four different kinds ground-truth networks. experimental results only show that outperforms competing but also demonstrate potential this inferring code are made freely available GitHub (https://github.com/nathanyl/scMGATGRN).

Язык: Английский

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circRNA-disease association prediction with an improved unbalanced Bi-Random walk

Journal of Radiation Research and Applied Sciences, Год журнала: 2024, Номер 17(2), С. 100858 - 100858

Опубликована: Фев. 23, 2024

Studies show that circular RNAs (circRNAs), a type of non-coding RNAs, play various roles in biological processes such as the formation and progression many different diseases. For this reason, identifying potential circRNAs associated with diseases is vital for early diagnosis. Determining these relationships experimentally requires long process also expensive. computational models are being developed to determine between circRNA In study, we recommend technique called Improved Unbalanced Bi-Random Walk (UBRW) identify The commonly used 5-fold cross-validation (CV) leave one-out (LOOCV) were applied verify predictive ability our technique. area under curve (AUC) values calculated CV LOOCV 0.8910 0.9669, respectively. Case studies on occurring gastric cancer breast conducted further validate performance method. When results examined, it was seen prediction UBRW method quite successful.

Язык: Английский

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Gene Expression Model for the Disease Prediction with Auto-Encoder Model with Classifiers

Journal of Biosciences and Medicines, Год журнала: 2025, Номер 13(03), С. 155 - 182

Опубликована: Янв. 1, 2025

Язык: Английский

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Design and Implementation of Takeaway Ordering Recommendation System Based on Python and Flask

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 277 - 287

Опубликована: Янв. 1, 2025

Язык: Английский

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Research on PM2.5 Concentration Prediction Based on SARIMA-RBF Concatenated Modeling

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 150 - 159

Опубликована: Янв. 1, 2025

Язык: Английский

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A New and Efficient IT Knowledge Exchange Platform

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 64 - 73

Опубликована: Янв. 1, 2025

Язык: Английский

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scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell RNA-Seq Clustering

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 52 - 63

Опубликована: Янв. 1, 2025

Язык: Английский

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Cluster Analysis of Scrna-Seq Data Combining Bioinformatics with Graph Attention Autoencoders and Ensemble Clustering

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 62 - 71

Опубликована: Янв. 1, 2024

Язык: Английский

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