{"entity": "researcher", "timestamp": "2026-08-22T07:48:32.248Z", "family": "Li", "given": "Zhong", "initials": "Z", "orcid": "0000-0002-2730-6427", "affiliations": ["School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China.", "School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/82e8eae189dc4774b04c3601d671e054.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/82e8eae189dc4774b04c3601d671e054"}}, "publications": [{"entity": "publication", "iuid": "30626eb250104327a789250841c9482f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/30626eb250104327a789250841c9482f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/30626eb250104327a789250841c9482f"}}, "title": "GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction.", "authors": [{"family": "Li", "given": "Zhong", "initials": "Z", "orcid": "0000-0002-2730-6427", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82e8eae189dc4774b04c3601d671e054.json"}}, {"family": "Jiang", "given": "Kaiyancheng", "initials": "K"}, {"family": "Qin", "given": "Shengwei", "initials": "S", "orcid": "0000-0001-6505-9650", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c1044c94ad164e2bb6a0ac9a5648dd38.json"}}, {"family": "Zhong", "given": "Yijun", "initials": "Y", "orcid": "0000-0001-8082-4103", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a5555247a5e442c790163f14dedd8e45.json"}}, {"family": "Elofsson", "given": "Arne", "initials": "A", "orcid": "0000-0002-7115-9751", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/248e70e81bd64f31a5f83e6e329bba95.json"}}], "type": "journal article", "published": "2021-06-00", "journal": {"title": "PLoS Comput Biol", "issn": "1553-7358", "volume": "17", "issue": "6", "pages": "e1009048", "issn-l": "1553-734X"}, "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.", "doi": "10.1371/journal.pcbi.1009048", "pmid": "34081706", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8205154"}, {"db": "pii", "key": "PCOMPBIOL-D-20-02104"}], "notes": [], "created": "2026-08-20T12:43:00.992Z", "modified": "2026-08-20T12:43:01.156Z"}, {"entity": "publication", "iuid": "fc9056dd181a431894328df284af4622", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/fc9056dd181a431894328df284af4622.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/fc9056dd181a431894328df284af4622"}}, "title": "Protein Contact Map Prediction Based on ResNet and DenseNet.", "authors": [{"family": "Li", "given": "Zhong", "initials": "Z", "orcid": "0000-0002-2730-6427", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82e8eae189dc4774b04c3601d671e054.json"}}, {"family": "Lin", "given": "Yuele", "initials": "Y"}, {"family": "Elofsson", "given": "Arne", "initials": "A"}, {"family": "Yao", "given": "Yuhua", "initials": "Y"}], "type": "journal article", "published": "2020-04-06", "journal": {"title": "Biomed Res Int", "issn": "2314-6141", "volume": "2020", "pages": "7584968", "issn-l": "2314-6133"}, "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.", "doi": "10.1155/2020/7584968", "pmid": "32337273", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC7165324"}], "notes": [], "created": "2026-08-20T12:03:22.267Z", "modified": "2026-08-20T12:03:22.354Z"}]}