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- G Yu#, K Yu#, X Wang#, X Huo*, Y Yang*. CLC-DB: an online open-source database of chiral ligands and catalysts. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-h2rdl.
- Y Xie#, Q Sang#, Q Da#, G Niu#, …, B Liu*, Y Yang*, W Dai*. Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression. Artificial Intelligence in Medicine (中科院1区,IF 7.5), 152 (2024) 102871.
- H Li#, Z Han#, …, H Chen*, Y Yang*, X Bo*. CGMega: Explainable Graph Neural Network Framework with Attention Mechanisms for Cancer Gene Module Dissection. Nature Communications (中科院1区,IF 16.6), 15:5997, https://doi.org/10.1038/s41467-024-50426-6.
- R Shi, G Yu, X Huo, Y Yang*. Prediction of chemical reaction yields with large-scale multi-view pre-training. Journal of Cheminformatics (IF 8.6), 16(22), 2024.
- H Wu, X Liu, Y Fang, Y Yang, Y Huang, X Pan, HB Shen*. Decoding protein binding landscape on circular RNAs with base-resolution Transformer models. Computers in Biology and Medicine (中科院1区,IF 7.7), 22:108175, 2024.
- Y Juan, G Niu, Y Yang, Y Dai, J Yang, J Zhang*. 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.
- Y Yang*, Y Tu, H Lei, W Long, HAMIL: Hierarchical Aggregation-based Multi-Instance Learning for Microscopy Image Classification. Pattern Recognition (中科院1区,IF 8.0), 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 (IF 4.3), 19 (11), e1011623.
- Y Juan#, G Niu#, Y Yang *, Y Dai *, J Zhang *, Y Han, and B Sun. Knowledge-aware design of high-strength aviation aluminum alloys via machine learning. Journal of Materials Research and Technology (中科院1区,IF 6.4), 24 (2023): 346-361.
- N Amin, J Liu, …Y Yang,…, CM Duijn*. Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals. JAMA psychiatry (中科院1区,IF 25.8), 80, no. 6 (2023): 597-609.
- 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 (IF 5.6), 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 (IF 5.8), 38(3):746–753, 2022
- Y Jin and Y Yang*, ProtPlat: an efficient pre-training platform for protein classification based on FastText, BMC Bioinformatics (IF 3.0), (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 (中科院1区,IF 9.5), 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 (IF 5.8), 2022, btac634
- J Hong, R Gao, Y Yang*, CrepHAN: Cross-species prediction of enhancers by using hierarchical attention networks. Bioinformatics (IF 5.8), 37(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 (IF 4.5), 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 (IF 5.6), 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 (IF 4.5), 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 (中科院1区,IF 9.5), 22, no. 6 (2021): bbab279.
- 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.
- 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.
- 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.
- 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
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- 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
- 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
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- 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.
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- Y Huang, W Lu, Y Yang*,An Efficient Prototype-Based Clustering Approach for Edge Pruning in Graph Neural Networks to Battle Over-Smoothing,International Joint Conference on Artificial Intelligence (IJCAI 2024)
- T Xia, Z He, T Ren, Y Miao, Z Zhang, Y Yang*, R Wang*,Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method,The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)
- 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 Y 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)
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