聂茹，女，博士（后），副教授，硕士生导师，中科院计算技术研究所访问学者。目前主要从事机器学习与数据挖掘、生物信息学、模式识别等方向的研究，参编教材3部，主持和参与校级教改项目7项，主持和参与包括国家重点研发计划、国家自然科学基金重点项目和面上项目、江苏省基础研究计划等在内的科研项目近20项，发表高水平学术论文50余篇，其中SCI、EI检索40余篇，3篇影响因子大于10，单篇最高影响因子13.994。
近年来部分学术论文列表：
[1] HHOMR: A Hybrid High-order Moment Residual Model for MiRNA-Disease Association Prediction [J]. Briefings in Bioinformatics. 2024, 25(5): bbae412. (SCI检索, JCR 一区，CCF B，通讯作者)
[2] Lightweight Coal Flow Foreign Object Detection Algorithm [C]. Proceedings of International Conference on Intelligent Computing 2024. (EI检索，CCF C，第一作者)
[3] LMGATCDA: Graph Neural Network with Labeling Trick for Predicting CircRNA-Disease Associations [J]. IEEE-ACM Transactions on Computational Biology and Bioinformatics. 2023. (SCI检索, JCR 一区，CCF B，通讯作者)
[4] Adversarial dense graph convolutional networks for single-cell classification [J]. Bioinformatics, 2023, 39(2): btad043. (SCI检索, JCR一区)
[5] Predicting miRNA-disease associations based on graph random propagation network and attention network [J]. Briefings in Bioinformatics, 2022. (SCI检索, 中科院一区, JCR一区, 通讯作者)
[6] Hierarchical graph attention network for miRNA-disease association prediction [J], Molecular Therapy, 2022. (SCI检索, 中科院一区, JCR一区, 通讯作者)
[7] 基于异质图注意力网络的miRNA与疾病关联预测算法 [J]. 电子学报, 2022, 50(6): 1428-1435. (EI检索, 中文高水平期刊, CCF A, 通讯作者)
[8] GCMCDTI: graph convolutional autoencoder framework for predicting drug-target interactions based on matrix completion [J]. Journal of Bioinformatics and Computational Biology. 2022, (SCI检索, 通讯作者)
[9] Prediction of MiRNA-Disease Association based on Higher-Order Graph Convolutional Networks [C]. Proceedings of the 18th International Conference on Intelligent Computing, 2022. (EI检索，CCF C, 通讯作者)
[10] GAEMDA: A Graph Auto-Encoder Model for MiRNA-Disease Associations Prediction [J]. Briefings in Bioinformatics, 2021. (SCI检索, 中科院一区, JCR一区, 通讯作者)
[11] Efficient framework for predicting miRNA-disease associations based on improved hybrid collaborative filtering [J]. BMC Medical Informatics and Decision Making. 2021. (SCI检索, 中科院三区, JCR三区, 第一作者)
[12] FCGCNMDA: Predicting MiRNA-Disease Associations by Apply-ing Fully Connected Graph Convolutional Networks [J]. Molecular Genetics and Genomics, 2020. (SCI检索, 通讯作者)
[13] Using discriminative vector machine model with 2DPCA to predict interactions among proteins [J]. BMC Bioinformatics, 2019. (SCI检索, 通讯作者, 中科院二区, JCR二区)
[14] Predicting miRNA-disease Associations based on Neighbor Selection Graph Attention Networks [J]. IEEE-ACM Transactions on Computational Biology and Bioinformatics. 2022, (SCI检索, 中科院二区, JCR一区)
[15] Image Classification Based on Deep Belief Network and YELM [C]. Lecture Notes in Computer Sciences. 2020. (EI检索, CCF C)
[16] LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction. ICIC 2019. 2019. (EI检索, CCF C)
[17] In silico prediction of drug-target interaction networks based on drug topological structure and protein sequences [J]. Scientific Reports. 2017. (SCI检索, 中科院二区, JCR一区)
[18] Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier [J]. Oncotarget. 2017. (SCI检索, 中科院一区, JCR一区)
[19] Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics [J]. International Journal of Molecular Sciences. 2016. (SCI检索, 中科院二区, JCR二区)
[20] DBM-ELM深层网络模型 [J]. 南京大学学报(自然科学版), 2017. (中文核心, 通讯作者)