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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MGCNSS: miRNA–disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(3)

Опубликована: Март 27, 2024

Abstract Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes development new medicine. Recently, network-based approaches have been widely proposed for inferring potential associations between miRNAs and diseases. However, these ignore importance different relations in meta-paths when learning embeddings Besides, they pay little attention to screening out reliable negative samples is crucial improving prediction accuracy. In this study, we propose a novel approach named MGCNSS with multi-layer graph convolution high-quality sample selection strategy. Specifically, first constructs comprehensive heterogeneous network by integrating miRNA disease similarity networks coupled their known association relationships. Then, employ automatically capture meta-path lengths learn discriminative representations After that, establishes highly set from unlabeled distance-based Finally, train under an unsupervised manner predict The experimental results fully demonstrate that outperforms all baseline methods on both balanced imbalanced datasets. More importantly, conduct case studies colon neoplasms esophageal neoplasms, further confirming ability detect candidate miRNAs. source code publicly available GitHub https://github.com/15136943622/MGCNSS/tree/master

Язык: Английский

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ACP-CLB: An Anticancer Peptide Prediction Model Based on Multichannel Discriminative Processing and Integration of Large Pretrained Protein Language Models

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

Опубликована: Фев. 19, 2025

Cancer affects millions globally, and as research advances, our understanding treatment of cancer evolve. Compared to conventional treatments with significant side effects, anticancer peptides (ACPs) have gained considerable attention. Validating ACPs through wet-lab experiments is time-consuming costly. However, numerous artificial intelligence methods are now used for ACP identification classification. These typically apply a uniform strategy all feature types, overlooking the potential benefits more specialized processing different types. In this paper, we propose framework based on multichannel discriminative processing, where neural networks applied process various optimizing their respective vectors. Additionally, leverage Large Pretrained Protein Language Models capture deeper sequence features, further enhancing model's performance. Contributions: To better validate overall performance generalization ability model, compared it state-of-the-art models using four data sets (AntiCp2Main, AntiCp2 Alternate, ACP740, cACP-DeepGram). The results show improvements across most metrics. proposed assists researchers in distinguishing identifying validates need distinct

Язык: Английский

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iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites

PLoS Computational Biology, Год журнала: 2024, Номер 20(8), С. e1012399 - e1012399

Опубликована: Авг. 22, 2024

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step molecular cell biology. Deep learning (DL)-based methods have been proposed to predict achieved impressive identification performance. However, those cannot effectively capture long-distance dependencies, utilize the information multiple features. To overcome limitations, we propose DL-based model iCRBP-LKHA using deep hybrid networks for identifying sites. adopts five encoding schemes. Meanwhile, neural network architecture, which consists large kernel convolutional (LKCNN), block attention module with one-dimensional convolution (CBAM-1D) bidirectional gating recurrent unit (BiGRU), can explore local information, global context features automatically. verify effectiveness iCRBP-LKHA, compared its performance shallow algorithms on 37 circRNAs datasets stringent datasets. And state-of-the-art datasets, 31 linear The experimental results not only show that outperforms other competing methods, but also demonstrate potential this RNA-RBP

Язык: Английский

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A comprehensive review of deep learning-based approaches for drug–drug interaction prediction

Briefings in Functional Genomics, Год журнала: 2025, Номер 24

Опубликована: Янв. 1, 2025

Abstract Deep learning models have made significant progress in the biomedical field, particularly prediction of drug–drug interactions (DDIs). DDIs are pharmacodynamic reactions between two or more drugs body, which may lead to adverse effects and great significance for drug development clinical research. However, predicting DDI through traditional trials experiments is not only costly but also time-consuming. When utilizing advanced Artificial Intelligence (AI) deep techniques, both developers users face multiple challenges, including problem acquiring encoding data, as well difficulty designing computational methods. In this paper, we review a variety methods, similarity-based, network-based, integration-based approaches, provide an up-to-date easy-to-understand guide researchers different fields. Additionally, in-depth analysis widely used molecular representations systematic exposition theoretical framework extract features from graph data.

Язык: Английский

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Unveiling Pharmacogenomics Insights into Circular RNAs: Toward Precision Medicine in Cancer Therapy

Biomolecules, Год журнала: 2025, Номер 15(4), С. 535 - 535

Опубликована: Апрель 5, 2025

Pharmacogenomics is revolutionizing precision medicine by enabling tailored therapeutic strategies based on an individual genetic and molecular profile. Circular RNAs (circRNAs), a distinct subclass of endogenous non-coding RNAs, have recently emerged as key regulators drug resistance, tumor progression, responses. Their covalently closed circular structure provides exceptional stability resistance to exonuclease degradation, positioning them reliable biomarkers novel targets in cancer management. This review comprehensive analysis the interplay between circRNAs pharmacogenomics, focusing their role modulating metabolism, efficacy, toxicity profiles. We examine how circRNA-mediated regulatory networks influence chemotherapy alter targeted therapy responses, impact immunotherapy outcomes. Additionally, we discuss emerging experimental tools bioinformatics techniques for studying circRNAs, including multi-omics integration, machine learning-driven biomarker discovery, high-throughput sequencing technologies. Beyond diagnostic potential, are being actively explored agents delivery vehicles. Recent advancements circRNA-based vaccines, engineered CAR-T cells, synthetic circRNA therapeutics highlight transformative potential oncology. Furthermore, address challenges standardization, reproducibility, clinical translation, emphasizing need rigorous validation frameworks facilitate integration into practice. By incorporating profiling pharmacogenomic strategies, this underscores paradigm shift toward highly personalized therapies. hold immense overcome enhance treatment optimize patient outcomes, marking significant advancement

Язык: Английский

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

BMC Biology, Год журнала: 2025, Номер 23(1)

Опубликована: Апрель 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.

Язык: Английский

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Multiscale graph equivariant diffusion model for 3D molecule design

Science Advances, Год журнала: 2025, Номер 11(16)

Опубликована: Апрель 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

Язык: Английский

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

BMC Biology, Год журнала: 2025, Номер 23(1)

Опубликована: Апрель 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.

Язык: Английский

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Drug-Target Interaction Prediction Based on Metapaths and Simplified Neighbor Aggregation

Methods, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

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