Prediction of lncRNA-miRNA interaction based on sequence and structural information of potential binding site

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: unknown, P. 142255 - 142255

Published: March 1, 2025

Language: Английский

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HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug–disease association prediction

BMC Biology, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 16, 2025

Drug-disease association (DDA) prediction aims to identify potential links between drugs and diseases, facilitating the discovery of new therapeutic potentials reducing cost time associated with traditional drug development. However, existing DDA methods often overlook global relational information provided by other biological entities, complex structure limiting correlations disease embeddings. In this study, we propose HNF-DDA, a subgraph contrastive-driven transformer-style heterogeneous network embedding model for prediction. Specifically, HNF-DDA adopts all-pairs message passing strategy capture network, fully integrating multi-omics information. also proposes concept contrastive learning local drug-disease subgraphs, high-order semantic nodes. Experimental results on two benchmark datasets demonstrate that outperforms several state-of-the-art methods. Additionally, it shows superior performance across different dataset splitting schemes, indicating HNF-DDA's capability generalize novel categories. Case studies breast cancer prostate reveal 9 out top 10 predicted candidate 8 have documented effects. incorporates strategies into embedding, enabling effective representations enriched information, while demonstrating significant applications in repositioning.

Language: Английский

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Multiscale graph equivariant diffusion model for 3D molecule design

Science Advances, Journal Year: 2025, Volume and Issue: 11(16)

Published: April 16, 2025

Three-dimensional molecular generation is critical in drug design. However, current methods often rely on point clouds or oversimplified interaction models, limiting their ability to accurately represent structures. To address these challenges, this paper proposes the multiscale graph equivariant diffusion model for 3D molecule design (MD3MD). MD3MD partitions conformations into graphs, assigning different weights capture atomic interactions across scales. This framework guides process, enabling high-quality generation. Experimental results demonstrate that excels both unconditional and conditional tasks, producing diverse, stable, innovative molecules meet specified conditions. Visualization highlights MD3MD’s learn domain-specific patterns generate distinct from existing datasets while maintaining distributional consistency. By effectively exploring chemical space, surpasses previous generating chemically diverse molecules, offering a notable advancement field of

Language: Английский

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NeuroPred-AIMP: Multimodal Deep Learning for Neuropeptide Prediction via Protein Language Modeling and Temporal Convolutional Networks

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

Neuropeptides are key signaling molecules that regulate fundamental physiological processes ranging from metabolism to cognitive function. However, accurate identification is a huge challenge due sequence heterogeneity, obscured functional motifs and limited experimentally validated data. Accurate of neuropeptides critical for advancing neurological disease therapeutics peptide-based drug design. Existing neuropeptide methods rely on manual features combined with traditional machine learning methods, which difficult capture the deep patterns sequences. To address these limitations, we propose NeuroPred-AIMP (adaptive integrated multimodal predictor), an interpretable model synergizes global semantic representation protein language (ESM) multiscale structural temporal convolutional network (TCN). The introduced adaptive fusion mechanism residual enhancement dynamically recalibrate feature contributions, achieve robust integration evolutionary local information. experimental results demonstrated proposed showed excellent comprehensive performance independence test set, accuracy 92.3% AUROC 0.974. Simultaneously, good balance in ability identify positive negative samples, sensitivity 92.6% specificity 92.1%, difference less than 0.5%. result fully confirms effectiveness strategy task recognition.

Language: Английский

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DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions

BMC Biology, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 23, 2025

Abstract Background Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due the high cost time-consuming nature of traditional wet lab experiments, analyzing circRNA-miRNA associations is often inefficient labor-intensive. Although some computational models been developed identify these associations, they fail capture deep collaborative features between interactions do not guide training feature extraction networks based on high-order relationships, leading poor prediction performance. Results To address issues, we innovatively propose novel graph collaboration learning method for interaction, called DGCLCMI. First, it uses word2vec encode sequences into word embeddings. Next, present joint model combines an improved neural filtering with network optimization. Deep interaction information embedded informative within sequence representations prediction. Comprehensive experiments three well-established datasets across seven metrics demonstrate our algorithm significantly outperforms previous models, achieving average AUC 0.960. In addition, case study reveals 18 out 20 predicted unknown CMI data points are accurate. Conclusions The DGCLCMI improves representation by capturing information, superior performance compared prior methods. It facilitates discovery sheds light their roles in physiological processes.

Language: Английский

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