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Träfflista för sökning "WFRF:(Elofsson Arne) ;pers:(Li Zhong)"

Sökning: WFRF:(Elofsson Arne) > Li Zhong

  • Resultat 1-4 av 4
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1.
  • Hu, Hailong, et al. (författare)
  • A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure
  • 2019
  • Ingår i: Applied Sciences. - : MDPI AG. - 2076-3417. ; 9:17
  • Tidskriftsartikel (refereegranskat)abstract
    • The prediction of protein secondary structure continues to be an active area of research in bioinformatics. In this paper, a Bi-LSTM based ensemble model is developed for the prediction of protein secondary structure. The ensemble model with dual loss function consists of five sub-models, which are finally joined by a Bi-LSTM layer. In contrast to existing ensemble methods, which generally train each sub-model and then join them as a whole, this ensemble model and sub-models can be trained simultaneously and the performance of each model can be observed and compared during the training process. Three independent test sets (e.g., data1199, 513 protein Cuff & Barton set (CB513) and 203 proteins from Critical Appraisals Skills Programme (CASP203)) are employed to test the method. On average, the ensemble model achieved 84.3% in Q(3) accuracy and 81.9% in segment overlap measure (SOV) score by using 10-fold cross validation. There is an improvement of up to 1% over some state-of-the-art prediction methods of protein secondary structure.
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2.
  • Kang, Yanlei, et al. (författare)
  • AFTGAN : prediction of multi-type PPI based on attention free transformer and graph attention network
  • 2023
  • Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 39:2
  • Tidskriftsartikel (refereegranskat)abstract
    • Motivation: Protein–protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and disease states. Although numerous methods have been proposed to predict PPIs, it is still challenging for interaction prediction between unknown proteins. In this study, a novel neural network named AFTGAN was constructed to predict multi-type PPIs. Regarding feature input, ESM-1b embedding containing much biological information for proteins was added as a protein sequence feature besides amino acid co-occurrence similarity and one-hot coding. An ensemble network was also constructed based on a transformer encoder containing an AFT module (performing the weight operation on vital protein sequence feature information) and graph attention network (extracting the relational features of protein pairs) for the part of the network framework.Results: The experimental results showed that the Micro-F1 of the AFTGAN based on three partitioning schemes (BFS, DFS and the random mode) on the SHS27K and SHS148K datasets was 0.685, 0.711 and 0.867, as well as 0.745, 0.819 and 0.920, respectively, all higher than that of other popular methods. In addition, the experimental comparisons confirmed the performance superiority of the proposed model for predicting PPIs of unknown proteins on the STRING dataset.Availability and implementation: The source code is publicly available at https://github.com/1075793472/AFTGAN.Supplementary information: Supplementary data are available at Bioinformatics online.
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3.
  • Li, Zhong, et al. (författare)
  • GCSENet : A GCN, CNN and SENet ensemble model for microRNA-disease association prediction
  • 2021
  • Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 17:6
  • Tidskriftsartikel (refereegranskat)abstract
    • Recently, an increasing number of studies have demonstrated that miRNAs are involved in human diseases, indicating that miRNAs might be a potential pathogenic factor for various diseases. Therefore, figuring out the relationship between miRNAs and diseases plays a critical role in not only the development of new drugs, but also the formulation of individualized diagnosis and treatment. As the prediction of miRNA-disease association via biological experiments is expensive and time-consuming, computational methods have a positive effect on revealing the association. In this study, a novel prediction model integrating GCN, CNN and Squeeze-and-Excitation Networks (GCSENet) was constructed for the identification of miRNA-disease association. The model first captured features by GCN based on a heterogeneous graph including diseases, genes and miRNAs. Then, considering the different effects of genes on each type of miRNA and disease, as well as the different effects of the miRNA-gene and disease-gene relationships on miRNA-disease association, a feature weight was set and a combination of miRNA-gene and disease-gene associations was added as feature input for the convolution operation in CNN. Furthermore, the squeeze and excitation blocks of SENet were applied to determine the importance of each feature channel and enhance useful features by means of the attention mechanism, thus achieving a satisfactory prediction of miRNA-disease association. The proposed method was compared against other state-of-the-art methods. It achieved an AUROC score of 95.02% and an AUPR score of 95.55% in a 10-fold cross-validation, which led to the finding that the proposed method is superior to these popular methods on most of the performance evaluation indexes. Author summary Identifying miRNA-disease associations accelerates the understanding towards pathogenicity, which is beneficial for the development of treatment tools for diseases. Different from existing methods, our GCSENet captures the deep relationship between miRNA and disease through three heterogeneous graphs (disease, gene and miRNA) to promote an accurate prediction result. We performed the 10-fold cross validation to evaluate the performance of GCSENet, which can outperform many classic methods. Furthermore, we carried out case studies on four important diseases, which were used to evaluate the performance of our model regarding to the associations with experimental evidences in literature. The result shows that most predicted miRNAs (48 for lung neoplasms, 48 for heart failure, 48 for breast cancer and 50 for glioblastoma) in the top 50 predictions were confirmed in HMDD v3.0. As a result, it shows that GCSENet can make reliable predictions and guide experiments to uncover more miRNA-disease associations.
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4.
  • Li, Zhong, et al. (författare)
  • Protein Contact Map Prediction Based on ResNet and DenseNet
  • 2020
  • Ingår i: BioMed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141. ; 2020
  • Tidskriftsartikel (refereegranskat)abstract
    • Residue-residue contact prediction has become an increasingly important tool for modeling the three-dimensional structure of a protein when no homologous structure is available. Ultradeep residual neural network (ResNet) has become the most popular method for making contact predictions because it captures the contextual information between residues. In this paper, we propose a novel deep neural network framework for contact prediction which combines ResNet and DenseNet. This framework uses 1D ResNet to process sequential features, and besides PSSM, SS3, and solvent accessibility, we have introduced a new feature, position-specific frequency matrix (PSFM), as an input. Using ResNet's residual module and identity mapping, it can effectively process sequential features after which the outer concatenation function is used for sequential and pairwise features. Prediction accuracy is improved following a final processing step using the dense connection of DenseNet. The prediction accuracy of the protein contact map shows that our method is more effective than other popular methods due to the new network architecture and the added feature input.
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  • Resultat 1-4 av 4

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