Personal Information

聂茹

  • E-Mail:
  • School/Department:计算机科学与技术学院
  • Education Level:With Certificate of Graduation for Doctorate Study
  • Gender:Female
  • Degree:Doctor
  • Professional Title:副教授*
  • Email:

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、江苏省基础研究计划等在内的纵横向科研项目近20项,发表高水平学术论文40余篇,其中SCI、EI检索30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] 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类推荐,通讯作者) 

[2] Adversarial dense graph convolutional networks for single-cell classification [J]. Bioinformatics, 2023, 39(2): btad043. (SCI检索, JCR一区)

[3] 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)

[4] 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)

[5] 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类推荐, 通讯作者)

[6] 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)

[7] 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,通讯作者)

[8] 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)

[9] 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)

[10] 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)

[11] Using discriminative vector machine model with 2DPCA to predict interactions among proteins [J]. BMC Bioinformatics, 2019. (SCI检索,  影响因子: 3.242, 通讯作者, 中科院二区, JCR二区)

[12] 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)

[13] Image Classification Based on Deep Belief Network and YELM [C]. Lecture Notes in Computer Sciences. 2020. (EI检索) 

[14] LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction. ICIC 2019. 2019. (EI检索)

[15] 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)

[16] 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)

[17] 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)

[18] Extreme learning machine: algorithm, theory and applications [J]. Artificial Intelligence Review. 2015. (SCI检索, 中科院二区, JCR一区)

[19] The latest research progress on spectral clustering [J]. Neural Computing and Applications. 2014. (SCI检索, 中科院二区, JCR一区)

[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)


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    2013.9- 2014.8

    中科院计算技术研究所  | 访问学者

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