教师名录

杨 旸 副教授

主页: [点击这里]

办公室电话:

办公地点:电信楼群3号楼

电子邮件:yangyang@google0123.com

实验室: 智能计算与智能系统重点实验室

  • 研究兴趣
  • 教育背景
  • 工作经验
  • 教授课程
  • 论文发表
  • 项目资助
  • 获奖信息
  • 学术服务
生物医学图像处理,生物序列的语言学建模,机器学习算法

2003/09 2009/06,上海交通大学,计算机软件与理论,硕博连读,工学博士

2007/09 – 2009/02,美国加州大学河滨分校,联合培养博士生

1999/09 2003/06,上海交通大学,计算机科学与技术,学士





2014/02 - 至今,上海交通大学,计算机科学与工程系

2012/10 - 2013/10,美国加州大学河滨分校,计算机科学与工程系,访问学者

2009/07 - 2014/02,上海海事大学,信息工程学院



2014春 算法基础

2014秋 离散数学 程序设计思想与方法

2015春 机器学习(研究生) 

2016~2019 春 机器学习(研究生、IEEE班)

Recent Publications

  • W Long, Y Yang*, HB Shen, ImPLoc: A multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images. Bioinformatics, 2019 (in press).
  • Y Yang, Q Fang, HB Shen, Predicting gene regulatory interactions based on spatial gene expression data and deep learning, PLOS Computational Biology, 15(9), 2019.
  • K Zhang, X Pan, Y Yang*, HB Shen, CRIP: predicting circRNA-RBP interaction sites using a codon-based encoding and hybrid deep neural networks. RNA, 2019, 25:1604-1615.
  • W Long, T Li, Y Yang*, HB Shen, FlyIT: Drosophila Embryogenesis Image Annotation based on Image Tiling and Convolutional Neural Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019 (in press).
  • Y Yang, M Zhou, Q Fang, Hong-Bin Shen. (2019). Annofly: Annotating drosophila embryonic images based on an attention-enhanced RNN model. Bioinformatics, Volume 35, Issue 16, 15 August 2019, Pages 2834–2842.
  • X Pan#, Y Yang#, C Xia, AH Mirza, HB Shen, Recent Methodology and Progress of Deep learning for RNA-protein interaction prediction. WIREs RNA, 2019:e1544. (# equal contribution)
  • D Wang, L Geng, Y Zhao, Y Yang, Y Huang, Y Zhang, HB Shen,Artificial intelligence-based multi-objective optimization protocol for protein structure refinement. Bioinformatics, 2019, btz544.
  • Y Yang, X Fu, W Qu, Y Xiao, HB Shen, MiRGOFS: A GO-based functional similarity measure for miRNAs, with applications to the prediction of miRNA subcellular localization and miRNA-disease association, Bioinformatics, Volume 34, Issue 20, 15 October 2018, Pages 3547–3556.
  • H Zhang#, Y Yang#, HB Shen, “Detection of Curvilinear Structure in Images by a Multi-Centered Hough Forest Method,” IEEE Access, 2018, vol 6
  • Z Cao#, X Pan#, Y Yang#, Y Huang, HB Shen, “The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier, Bioinformatics, Volume 34, Issue 13, 1 July 2018, Pages 2185–2194.
  • K Liu, Y Yang, Incorporating Link Information in Feature Selection for Identifying Tumor Biomarkers by Using miRNA-mRNA Paired Expression Data, Current Proteomics 15 (2), 165-171
  • Y Yang, Z Wu, W Kong. “Improving clustering of microRNA microarray data by incorporating functional similarity”. Current bioinformatics, 13(1),34-41, 2018
  • Y Xiao, J Cai, Y Yang*, H Zhao, HB Shen, Prediction of MicroRNA Subcellular Localization by Using a Sequence-to-Sequence Model, in Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM’18), Singapore.
  • G Ji, Y Yang*, HB Shen, "IterVM: An Iterative Model for Single-Particle Cryo-EM Image Clustering Based on Variational Autoencoder and Multi-Reference Alignment”, The IEEE International Conference on Bioinformatics and Biomedicine (BIBM’18), Madrid, Spain
  • T Li, Y Yang*, HB Shen,“HMIML: Hierarchical Multi-Instance Multi-Label Learning of Drosophila Embryogenesis Images Using Convolutional Neural Networks” The IEEE International Conference on Bioinformatics and Biomedicine (BIBM’18), Madrid, Spain
  • Y Yang, Y Xiao, T Cao, W Kong, “MiRFFS: a functional group-based feature selection method for the identification of microRNA biomarkers”, Int. J. Data Mining and Bioinformatics, vol. 18(1), 2017
  • Y Yang, N Huang, L Hao, W Kong, “A clustering-based approach for the identification of microRNA combinatorial biomarkers”, BMC Genomics, 18 (2), 210, 2017
  • H Zhou#, Y Yang#, HB Shen, “Hum-mPLoc 3.0: Prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features,” Bioinformatics, 33(6), 843-853, 2016
  • Y Yang*, Z. Xu, D Song, “Missing value imputation for microRNA expression data by using a GO-based similarity measure”, BMC bioinformatics, 2016,17(1):10

  • 国家自然科学基金面上项目,61972251,“基于多模态机器学习的非编码RNA定位预测”, 主持;
  • 上海市自然科学基金(探索类)“探索可靠筛选microRNA生物标志物的途径与方法” (No. 16ZR1448700), 主持;
  • 国家自然科学基金青年项目,61003093,“革兰氏阴性菌III型分泌系统效应蛋白的计算  预测研究”,主持;
  • 教育部留学回国人员科研启动基金,“植物防御反应中转录与转录后协同调控网络研究”,主持;
  • 上海交通大学新教师科研启动基金,“用于癌症早期诊断与转移类型识别的肿瘤标志物甄选与作用机理研究”,主持;
  • 上海高校选拔培养优秀青年教师科研专项基金资助项目,“基于机器学习方法的蛋白质分类研究”,主持;
  • 国家重点研发计划“精准医学大数据的有效挖掘与关键信息技术研发”(2018-2020),科研骨干。







联系我们 webmaster@google0123.com

上海交通大学计算机科学与工程系版权所有 @ 2013

电脑访问:西瓜视频免费观看 PC端 | 手机访问:西瓜视频免费观看 手机端
友情链接: