宁乔

基本信息：

宁乔，博士，副教授，硕士生导师。2019年毕业于东北师范大学。2020年1月至2024年6月任职于大连海事大学信息科学技术学院，2025年1月任职江南大学人工智能与计算机技术学院副教授。曾于美国加州大学旧金山分校进行访学，CCF 会员， CAAI会员。

研究方向：

人工智能、AI+生物信息学、生物医学数据挖掘

主要成果：

已围绕蛋白质功能预测、基因调控网络预测及疾病关联网络预测等研究问题在国际著名期刊和国际会议（如NeurIPS，BIOINFORMATICS、BIB、IEEE TCBB、IEEE TCE、BIBM、ICDM等）上发表论文50余篇，申请/授权国家发明专利10余项。主持国家自然科学基金青年项目、大连市青年科技之星、吉林大学符号计算与知识工程教育部重点实验室开放课题等课题项目。

招生专业：

专硕：计算机科学与技术、软件工程

学硕：软件工程、人工智能、计算机技术

Email：ningq669@jiangnan.edu.cn

部分研究成果：

[1]Zhanchang Zhang,Qiao Ning*,Xulun Shi, et al. A Directional Multi-LSTM Framework Integrated BERT for S-sulfhydration Sites Prediction.Chemometrics and Intelligent Laboratory Systems, 2026:105653.(JCR 1区)

[2]Qiao Ning, Shaohang Qiao, Yawen Cai, et al.Geometry-Enhanced Multiscale Joint Representation Learning for Drug-Target Interaction Prediction.Journal of Chemical Information and Modeling, 2026,66(3):1906-1919.(JCR 1区，TOP期刊)

[3] Yaomiao Zhao, Shaohang Qiao,Qiao Ning*, et al.Graph Clustering-guided Multi-view Neighborhood-enhanced Graph Contrastive Learning for Drug-Target Interaction Prediction.IEEE Journal of Biomedical and Health Informatics, 2026,30(3):2194-2202.(JCR 1区，TOP期刊)

[4]Xinyu Li, Ruijie Li,Qiao Ning*,et al. Molecular-driven Multi-view Hypergraph Contrastive Learning for Drug-Drug Interaction Prediction.IEEE Transactions on Computational Biology and Bioinformatics, 2026, DOI:10.1109/TCBBIO.2026.3672913.(CCF推荐B类期刊)

[5]Qiao Ning, Yanpeng Liu, Shaohang Qiao, et al.ZNGEA: ZINB-NMF Integrated Graph Embedding Autoencoder for Metabolite-Disease Association Identification.Analytical Chemistry, 2025,97(50): 27931-27940.(JCR 1区，TOP期刊)

[6] Xirun Wei,Qiao Ning*, Kuiyang Che, et al.Sul-BertGRU: an ensemble deep learning method integrating information entropy-enhanced BERT and directional multi-GRU for S-sulfhydration sites prediction.Bioinformatics, 2025,41(3): btaf078.(CCF推荐A类期刊)

[7]Qiao Ning, Yue Wang, Yaomiao Zhao, et al.DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction,Artificial Intelligence in Medicine. 2025,159: 103023.(CCF推荐C类期刊，TOP期刊)

[8] Kuiyang Che,Qiao Ning*, Ruijie Li, et al. Cross Attention and Intra-layer Attention in Heterogeneous Graph Neural Networks for Drug-Target Interaction Prediction.IEEE Transactions on Computational Biology and Bioinformatics, 2025, 1:1-12.(CCF推荐B类期刊)

[9] Ruijie Li,Qiao Ning*, Yaomiao Zhao, et al.MHMDA: Similarity- Association-Similarity Metapaths and Heterogeneous-Hyper Network Learning for MiRNA-Disease Association Prediction.IEEE Transactions on Computational Biology and Bioinformatics, 2025,22(1): 203-215.(CCF推荐B类期刊)

[10]Qiao Ning, Chaorui Guo, Ming Wu, et al.Prediction of miRNA-disease Association based on Biodiversity Association Network.IEEE Transactions on Computational Biology and Bioinformatics, 2025, 22(6):2515 - 2525.(CCF推荐B类期刊)

[11] Yanxin Chen,Qiao Ning*, Yitong Zhang, et al.Dual-network cross-learning for metabolite-disease association prediction.IEEE Transactions on Computational Biology and Bioinformatics, 2025,22(2): 545-556.(CCF推荐B类期刊)

[12]Qiao Ning, Yaomiao Zhao, Jun Gao, et al.Hierarchical hypergraph learning in association-weighted heterogeneous network for miRNA-disease association identification.IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024,21(6): 2531-2542.(CCF推荐B类期刊)

[13] Jun Gao, Yaomiao Zhao, Chen Chen,Qiao Ning*.MVNN-HNHC: A multi-view neural network for identification of human non-histone crotonylation sites.Analytical Biochemistry, 2024,687: 115426.(SCI)

[14]Qiao Ning, Yaomiao Zhao, Jun Gao, et al.AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA-disease associations identification.Briefings in Bioinformatics, 2023,24(2): bbad094.(CCF推荐B类期刊，IF₂₀₂₃=9.5，TOP期刊)

[15]Qiao Ning, Yue Wang, Zedong Qi.SEBP_HNHC: Stacking Ensemble-based Bi-level Predictor for Human Non-Histone Crotonylation combining with iterative feature representation strategy.Chemometrics and Intelligent Laboratory Systems, 2023,243: 105019.(JCR 1区)

[16]Qiao Ning, Jinmou Li.DLF-Sul: a multi-module deep learning framework for prediction of S-sulfinylation sites in proteins.Briefings in Bioinformatics, 2022,23(5): bbac323.(CCF推荐B类期刊，IF₂₀₂₂=13.994，TOP期刊)

[17] Lu Jiang, Jiahao Sun, Yue Wang,Qiao Ning*, et al.Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities,Briefings in Bioinformatics, 2022,23(2): bbac016.(CCF推荐B类期刊，IF₂₀₂₂=13.994，TOP期刊)

[18]Qiao Ning, Zedong Qi, Yue Wang, et al. FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites.Briefings in Bioinformatics, 2022,23(6): bbac421.(CCF推荐B类期刊，IF₂₀₂₂=13.994，TOP期刊)

[19]Qiao Ning, Xiaowei Zhao, Zhiqiang Ma.A novel method for Identification of Glutarylation sites combining Borderline-SMOTE with Tomek links technique in imbalanced data.IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021,19(5): 2632-2641.(CCF推荐B类期刊)

[20]Qiao Ning, Mingyu Sheng.m7G-DLSTM: Intergrating directional Double-LSTM and fully connected network for RNA N7-methlguanosine sites prediction in human.Chemometrics and Intelligent Laboratory Systems, 2021,217: 104398.(JCR 1区)

[21]Qiao Ning, Ansheng Deng, Tingting Zou, et al.pQLyCar: Peptide-based dynamic query-driven sample rescaling strategy for identifying carboxylation sites combined with KNN and SVM.Analytical Biochemistry, 2021,633: 114386.(SCI)