DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions DOI Creative Commons
Chao Cao, Mengli Li, Chunyu Wang

et al.

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

AIPs-DeepEnC-GA: Predicting Anti-inflammatory Peptides using Embedded Evolutionary and Sequential Feature Integration with Genetic Algorithm based Deep Ensemble Model DOI
Ali Raza, Jamal Uddin, Quan Zou

et al.

Chemometrics and Intelligent Laboratory Systems, Journal Year: 2024, Volume and Issue: unknown, P. 105239 - 105239

Published: Sept. 1, 2024

Language: Английский

Citations

22
A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation DOI Creative Commons
Mengting Niu, Chunyu Wang, Zhanguo Zhang

et al.

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

Citations

15
DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model DOI Creative Commons
Yihe Pang,

Bin Liu

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

Published: Jan. 2, 2024

Abstract Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, their functions involve binding interactions with partners remaining native structural flexibility. The rapid increase in the number sequence databases diversity challenge existing computational methods for predicting protein intrinsic disorder functions. A region interacts different to perform multiple these dependencies correlations. In this study, we introduce DisoFLAG, method leverages graph-based interaction language model (GiPLM) jointly its potential GiPLM integrates semantic information based on pre-trained models into units enhance correlation representation DisoFLAG predictor takes amino acid sequences only inputs provides predictions six proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, flexible linker. We evaluated predictive performance following Critical Assessment Intrinsic Disorder (CAID) experiments, results demonstrated offers accurate comprehensive extending current coverage computationally predicted function categories. standalone package web server have been established provide prediction tools disorders associated

Language: Английский

Citations

14
GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction DOI Creative Commons

Mengmeng Gao,

Daokun Zhang,

Yi Chen

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 173, P. 108339 - 108339

Published: March 18, 2024

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the discovery process, significantly lower economic cost and time consumption than traditional pipeline. With great power AI, it is possible rapidly search vast chemical space for drug-target interactions (DTIs) between candidate disease protein targets. However, only a small proportion have labelled DTIs, consequently limiting performance AI-based screening. To solve this problem, machine learning-based approach ability generalize DTI prediction across desirable. Many existing learning approaches identification failed exploit full information respect topological structures molecules. develop better prediction, we propose GraphormerDTI, which employs powerful Graph Transformer neural network model molecular structures. GraphormerDTI embeds graphs into vector-format representations through iterative Transformer-based message passing, encodes molecules' structural characteristics by node centrality encoding, spatial encoding edge encoding. strong inductive bias, proposed can effectively infer informative out-of-sample as such, capable predicting DTIs an exceptional performance. integrates 1-dimensional Convolutional Neural Network (1D-CNN) extract drugs' target proteins' leverages attention mechanism them. examine GraphormerDTI's conduct experiments on three benchmark datasets, where achieves superior five state-of-the-art baselines out-of-molecule including GNN-CPI, GNN-PT, DeepEmbedding-DTI, MolTrans HyperAttentionDTI, par best baseline transductive prediction. source codes datasets are publicly accessible at https://github.com/mengmeng34/GraphormerDTI.

Language: Английский

Citations

14
Prediction of blood–brain barrier penetrating peptides based on data augmentation with Augur DOI Creative Commons
Zhi-Feng Gu,

Yu-Duo Hao,

Tianyu Wang

et al.

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

Published: April 19, 2024

Abstract Background The blood–brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, tightly connected basal membranes. It plays pivotal role in safeguarding from harmful substances, thus protecting integrity nervous system preserving overall homeostasis. However, this remarkable selective transmission also poses formidable challenge realm central diseases treatment, hindering delivery large-molecule drugs into brain. In response to challenge, many researchers have devoted themselves developing drug systems capable breaching barrier. Among these, penetrating peptides emerged promising candidates. These had advantages high biosafety, ease synthesis, exceptional penetration efficiency, making them an effective solution. While previous studies developed few prediction models for peptides, their performance has often been hampered by issue limited positive data. Results study, we present Augur, novel model using borderline-SMOTE-based data augmentation machine learning. extract highly interpretable physicochemical properties while solving issues small sample size imbalance negative samples. Experimental results demonstrate superior Augur with AUC value 0.932 on training set 0.931 independent test set. Conclusions This newly demonstrates predicting offering valuable insights development targeting neurological disorders. breakthrough may enhance efficiency peptide-based discovery pave way innovative treatment strategies diseases.

Language: Английский

Citations

12
Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation DOI Creative Commons

Huan Zhu,

Hongxia Hao, Liang Yu

et al.

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

Published: Aug. 15, 2024

Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance human microbiome in relation to health. Identifying microbes associated with diseases is crucial for early disease diagnosis advancing precision medicine. Considering that information about changes microbial quantities under fine-grained states helps enhance a comprehensive understanding overall data distribution, this study introduces MSignVGAE, framework predicting microbe-disease sign associations using signed message propagation. MSignVGAE employs graph variational autoencoder model noisy association extends multi-scale concept representation capabilities. A novel strategy propagating networks addresses heterogeneity consistency among nodes connected by edges. Additionally, we utilize idea denoising handle noise similarity feature information, which overcome biases fused data. represents as heterogeneous node features. The multi-class classifier XGBoost utilized predict between microbes. achieves AUROC AUPR values 0.9742 0.9601, respectively. Case studies on three demonstrate can effectively capture distribution leveraging information.

Language: Английский

Citations

12
CODENET: A deep learning model for COVID-19 detection DOI

Hong Ju,

Yanyan Cui,

Qiaosen Su

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 171, P. 108229 - 108229

Published: Feb. 29, 2024

Language: Английский

Citations

11
MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier DOI Creative Commons
Hongqi Zhang,

Shanghua Liu,

Rui Li

et al.

ACS Omega, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 8, 2024

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to ions the type of protein, this study proposed classifier named MIBPred. The incorporated advanced Word2Vec technology from field natural language processing extract semantic features sequence combined them position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for classification task. model, we independently trained XGBoost, LightGBM, CatBoost algorithms integrated output results through SVM voting mechanism. This innovative combination has led significant breakthrough predictive performance our model. As result, achieved accuracies 95.13% 85.19%, respectively, predicting their types. Our research not only confirms effectiveness extracting information sequences but also highlights outstanding MIBPred problem provides reliable tool method in-depth exploration structure function proteins.

Language: Английский

Citations

10
Integrated convolution and self-attention for improving peptide toxicity prediction DOI Creative Commons
Shihu Jiao, Xiucai Ye, Tetsuya Sakurai

et al.

Bioinformatics, Journal Year: 2024, Volume and Issue: 40(5)

Published: May 1, 2024

Abstract Motivation Peptides are promising agents for the treatment of a variety diseases due to their specificity and efficacy. However, development peptide-based drugs is often hindered by potential toxicity peptides, which poses significant barrier clinical application. Traditional experimental methods evaluating peptide time-consuming costly, making process inefficient. Therefore, there an urgent need computational tools specifically designed predict accurately rapidly, facilitating identification safe candidates drug development. Results We provide here novel approach, CAPTP, leverages power convolutional self-attention enhance prediction from amino acid sequences. CAPTP demonstrates outstanding performance, achieving Matthews correlation coefficient approximately 0.82 in both cross-validation settings on independent test datasets. This performance surpasses that existing state-of-the-art predictors. Importantly, maintains its robustness generalizability even when dealing with data imbalances. Further analysis reveals certain sequential patterns, particularly head central regions crucial determining toxicity. insight can significantly inform guide design safer drugs. Availability implementation The source code freely available at https://github.com/jiaoshihu/CAPTP.

Language: Английский

Citations

9
msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths DOI Creative Commons
Yazi Li, Xiaoman Wei, Qinglin Yang

et al.

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

Published: May 30, 2024

A promoter is a specific sequence in DNA that has transcriptional regulatory functions, playing role initiating gene expression. Identifying promoters and their strengths can provide valuable information related to human diseases. In recent years, computational methods have gained prominence as an effective means for identifying promoter, offering more efficient alternative labor-intensive biological approaches.

Language: Английский

Citations

9