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Journal Publications

2024

  • H Li, Z Han, …, Y Yang *, X Bo *. CGMega: Explainable Graph Neural Network Framework with Attention Mechanisms for Cancer Gene Module Dissection. Nature Communications (2024), to appear.
  • R Shi, G Yu, X Huo, Y Yang*. Prediction of chemical reaction yields with large-scale multi-view pre-training. Journal of Cheminformatics 16, 22 (2024).
  • Wu H, Liu X, Fang Y, Yang Y, Huang Y, Pan X, Shen HB. Decoding protein binding landscape on circular RNAs with base-resolution Transformer models. Computers in Biology and Medicine. 2024 Feb 22:108175.
  • Juan Y, Niu G, Yang Y, Dai Y, Yang J, Zhang J. Machine learning-based identification method of new strengthening element and the study on Al-Zn-Mg-Cu-Zr-Hf alloy. Materials Today Communications. 2024 Feb 10:108359.

2023

  • Y Yang*, Y Tu, H Lei, W Long, HAMIL: Hierarchical Aggregation-based Multi-Instance Learning for Microscopy Image Classification. Pattern Recognition, vol 136, 2023, 109245.
  • Y Huang, G Yu, Y Yang*，MIGGRI: a multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images，PLoS Computational Biology，19 (11), e1011623.
  • Yong-fei, Juan, Niu Guo-shuai, Yang Yang *, Dai Yong-bing *, Zhang Jiao *, Han Yan-feng, and Sun Bao-de. “Knowledge-aware design of high-strength aviation aluminum alloys via machine learning.” Journal of Materials Research and Technology 24 (2023): 346-361.
  • Amin, Najaf, Jun Liu, …Yang Yang,…, Cornelia M van Duijn. “Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals.” JAMA psychiatry 80, no. 6 (2023): 597-609.

2022

  • Z Ji, R Shi, J Lu, F Li, Y Yang* , ReLMole: Molecular Representation Learning based on Two-Level Graph Similarities , Journal of Chemical Information and Modeling, 2022, 62, 22, 5361–5372
  • L Zheng, Z Liu, Y Yang*, HB Shen, Accurate inference of gene regulatory interactions from spatial gene expression with deep contrastive learning. Bioinformatics, 38(3):746–753, 2022
  • Y Jin and Y Yang*, ProtPlat: an efficient pre-training platform for protein classification based on FastText, BMC Bioinformatics (2022) 23:66.
  • Y Tu, H Lei, HB Shen, and Y Yang*. SIFLoc: a self-supervised pre-training method for enhancing the recognition of protein subcellular localization in immunofluorescence microscopic images. Briefings in Bioinformatics 23, no. 2 (2022): bbab605.
  • P Zhang, M Zhang, H Liu, Y Yang*, Prediction of protein subcellular localization based on microscopic images via multi-task multi-instance learning, Chinese Journal of Electronics 31(5):1-9, 2022.
  • J Hu, Y Yang, YY Xu, HB Shen, GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images, Bioinformatics, 2022, btac634

2021

  • J Hong, R Gao, Y Yang*, CrepHAN: Cross-species prediction of enhancers by using hierarchical attention networks. Bioinformatics 37, no. 20 (2021): 3436-3443.
  • Z Yu, J Lu, Y Jin, Y Yang*, KenDTI: an Ensemble Model for Predicting Drug-target Interaction by Integrating Multi-source Information. IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 1305-1314, vol. 18, 2021
  • Y Jin, J Lu, R. Shi, Y Yang*. EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction. Biomolecules 2021, 11, 1783.
  • Y Chen, R Xie, Y Yang, L He, D Feng, and HB Shen. “Fast Cryo-EM Image Alignment Algorithm Using Power Spectrum Features.” Journal of Chemical Information and Modeling 61, no. 9 (2021): 4795-4806.
  • J Hu, Y Yang, Y Xu, HB Shen, Incorporating label correlations into deep neural networks to classify protein subcellular location patterns in immunohistochemistry images. Proteins: Structure, Function, and Bioinformatics, 90(2), 493-503, 2021
  • 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, 18(1):194-204, 2021.1
  • Y Juan, Y Dai, Y Yang, J Zhang, Accelerating materials discovery using machine learning. Journal of Materials Science & Technology 79, 178-190,2021
  • L Yuan, Y Yang*, DeCban: Prediction of circRNA-RBP Interaction Sites by Using Double Embeddings and Cross-Branch Attention Networks. Front. Genet. 11:632861. 2021.1
  • H Wu, X Pan, Y Yang, and HB Shen, Recognizing binding sites of poorly characterized RNA-binding proteins on circular RNAs using attention Siamese network. Briefings in bioinformatics 22, no. 6 (2021): bbab279.

2020

  • W Long, Y Yang*, HB Shen, ImPLoc: A multi-instance deep learning model for protein subcellular localization based on immunohistochemistry images. Bioinformatics, 2020, 36: 2244-2250.
  • X Pan, Y Fang, X Li, Y Yang, HB Shen, RBPsuite: RNA-protein binding sites prediction suite based on deep learning. BMC Genomics, 2020, 21:1-8.
  • R Xie, Y Chen, J Cai, Y Yang, HB Shen, SPREAD: A Fully Automated Toolkit for Single-Particle Cryogenic Electron Microscopy Data 3D Reconstruction with Image-Network-Aided Orientation Assignment. Journal of Chemical Information and Modeling, 2020, 60: 2614-2625.
  • Y Guo, Y Yang, Y Huang, HB Shen, Discovering Nuclear Targeting Signal Sequence through Protein Language Learning and Multivariate Analysis. Analytical Biochemistry, 2020, 591: 113565.
  • SH Feng, WX Zhang, J Yang, Y Yang, HB Shen, Topology prediction improvement of α-helical transmembrane proteins through helix-tail modeling and multiscale deep learning fusion. Journal of Molecular Biology, 2020, 432: 1279-1296.
  • 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, 2020, 36: 437-448.

2019

  • Y Yang, Q Fang, HB Shen, Predicting gene regulatory interactions based on spatial gene expression data and deep learning, PLoS Comput Biol 15(9): e1007324.
  • 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 Fu, Y Yang*, WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning, Quantitative Biology, 2019(4).
  • Y Ju, L Yuan, Y Yang*, H Zhao, CircSLNN: Identifying RBP-binding sites on circRNAs via sequence labeling neural networks, Frontiers in Genetics, vol 10, article 1184, 2019.11.22
  • 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
  • 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)
  • S Yin, B Zhang, Y Yang, Y Huang, HB Shen, Clustering enhancement of noisy cryo-electron microscopy single-particle images with a network structural similarity metric. Journal of Chemical Information and Modeling, 2019, Volume 59, Issue 4, 1658-1667.

2018

  • 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
  • X Yin, J Yang, F Xiao, Y Yang, H Shen, MemBrain: An easy-to-use online webserver for transmembrane protein structure prediction, Nano-Micro Letters 10 (1), 2.
  • Y Yang, Z Wu, W Kong. “Improving clustering of microRNA microarray data by incorporating functional similarity”. Current bioinformatics, 13(1),34-41, 2018

2017

  • H Zhang#, Y Yang#, HB Shen, “Line Junction Detection Without Prior-Delineation of Curvilinear Structure in Biomedical Images,” IEEE Access, 2017, 6：2016-2027
  • 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
  • W Kong, X Mou, J Deng, B Di, R Zhong, S Wang, Y Yang, W Zeng, Differences of immune disorders between Alzheimer’s disease and breast cancer based on transcriptional regulation, PloS one 12 (7), e0180337, 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

Selected publications before 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
  • J Wong, L Gao, Y Yang, W Ma, et al., “Roles of small RNAs in soybean defense against Phytophthora sojae infection,” The Plant Journal, 2014, doi: 10.1111/tpj.12590
  • Y Yang, S Qi, “A new feature selection method for computational prediction of type III secreted effectors”, International Journal of Data Mining and Bioinformatics, vol. 10, no. 4, 2014.
  • BL Lu, X Wang, Y Yang, H Zhao, “Learning from imbalanced data sets with a Min-Max modular support vector machine,” Frontiers of Electrical and Electronic Engineering, vol. 6(1), pp. 56-71, 2011
  • Y Yang, J Zhao, RL Morgan, W Ma, T Jiang, “Computational prediction of type III secreted proteins from gram-negative bacteria,” BMC Bioinformatics, 2010, 11(S1):S47
  • Y Yang and BL Lu, “Protein subcellular multi-localization prediction using a min-max modular support vector machine,” International Journal of Neural Systems, vol. 20, No. 1, pp. 13-28, 2010.
  • D Song#, Y Yang#, B Yu, B Zheng, Z Deng, BL Lu, X Chen and T Jiang, “Computational prediction of novel non-coding RNAs in Arabidopsis thaliana”, BMC Bioinformatics 2009, 10(S1):S36

Selected Conference Publications

  • W Lu, … , Y Yang*，UPCoL: Uncertainty-Informed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation，International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023)，
  • Z Han, G Yu, and Yang Yang*，Enhancing Cancer Gene Prediction through Aligned Fusion of Multiple PPI Networks Using Graph Transformer Models，2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023)
  • J Xiang, P Qiu, Y Yang*, FUSSNet: Fusing Two Sources of Uncertainty for Semi-Supervised Medical Image Segmentation, 25th International Conference on Medical Image, Computing and Computer Assisted Intervention, MICCAI 2022
  • Y Xie, G Niu, Q Da, W Dai, Y Yang*, “Survival Prediction for Gastric Cancer via Multimodal Learning of Whole Slide Images and Gene Expression”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022
  • Y Huang, S Dong, D Wang, C Wan, Y Yang*, “Learning Time-Series Images of Niacin Skin-Flushing Test for the Diagnosis of Schizophrenia and Affective Disorder”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022
  • R Hu, J Cai, W Zheng, Y Yang*, HB Shen, NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing. 583-588, The IEEE International Conference on Bioinformatics and Biomedicine (BIBM’20)
  • 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
  • X Fu, Y Xiao, Y Yang*, “Prediction of Type III Secreted Effectors Based on Word Embeddings for Protein Sequences”, in Proc. International symposium on bioinformatics research and applications, ISBRA 2018
  • Y Yang, T Cao, W Kong, “Feature selection based on functional group structure for microRNA expression data analysis”, the 2016 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2016)