# paper_repository **Repository Path**: fartancy/paper_repository ## Basic Information - **Project Name**: paper_repository - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 7 - **Created**: 2021-12-30 - **Last Updated**: 2021-12-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README 说明 将论文进行分类，每个文件夹放同一个类型论文，在readme中建列出（作者，论文标题，期刊，年份，下载链接） # 个人博客： 张桂阳：[链接](https://blog.csdn.net/ZGY9542?spm=1000.2115.3001.5343) 黄国华：[链接](https://blog.csdn.net/freemanguohua) 王攀：[链接](hhttps://mp.csdn.net/) # Graph neural network 1. Defferrard, Michaël, Xavier Bresson, and Pierre Vandergheynst. "Convolutional neural networks on graphs with fast localized spectral filtering." Advances in Neural Information Processing Systems. 2016. [download](https://gitee.com/bio-papers/paper_repository/raw/master/huangguohua/Convolutional%20neural%20networks%20on%20graphs%20with%20fast%20localized%20spectral%20filtering.pdf) # RNA modification 1.Li J, Huang Y, Cui Q, et al. m6Acorr: an online tool for the correction and comparison of m 6 A methylation profiles[J]. BMC bioinformatics, 2020, 21(1): 1-8.[download](https://gitee.com/fartancy/paper_repository/raw/master/RNA%20modification/m6Acorr%EF%BC%9Aan%20online%20tool%20for%20the%20correction%20and%20comparison%20of%20m6%20A%20methylation%20profiles.pdf) # protein modification # protein-protein interaction 1.Qiao Y, Zhu X, Gong H. BERT-KCR: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models[J]. Bioinformatics, 2021.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/BERT-Kcr%20Prediction%20of%20lysine%20crotonylation%20sites%20by%20a%20transfer%20learning%20method%20with%20pretrained%20BERT%20models.pdf) 2.Bi Y, Xiang D, Ge Z, et al. An interpretable prediction model for identifying N7-methylguanosine sites based on XGBoost and SHAP[J]. Molecular Therapy-Nucleic Acids, 2020, 22: 362-372.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/An%20Interpretable%20Prediction%20Model-Methylguanosine%20Sites%20Based%20on%20XGBoost%20and%20SHAP.pdf) 3.Lu S, Li Y, Nan X, et al. Attention-based convolutional neural networks for protein-protein interaction site prediction[J]. bioRxiv, 2021.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/Attention-based%20convolutional%20neural%20networks%20for%20protein-protein%20interaction%20site%20prediction.pdf) 4.Zhang J, Ghadermarzi S, Katuwawala A, et al. DNAgenie: accurate prediction of DNA-type-specific binding residues in protein sequences[J]. Briefings in Bioinformatics, 2021, 22(6): bbab336.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/DNAgenie%20accurate%20prediction%20of%20DNA-type-specific%20binding%20residues%20in%20protein%20sequences.pdf) 5.Zhang Y, Lin J, Zhao L, et al. A novel antibacterial peptide recognition algorithm based on BERT[J]. Briefings in Bioinformatics, 2021.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/A%20novel%20antibacterial%20peptide%20recognition%20algorithm%20based%20on%20BERT.pdf) 6.Warikoo N, Chang Y C, Hsu W L. LBERT: Lexically aware Transformer-based Bidirectional Encoder Representation model for learning universal bio-entity relations[J]. Bioinformatics, 2021, 37(3): 404-412.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/LBERT%20Lexically%20aware%20Transformer-based%20Bidirectional.pdf) 7.Xie Z, Deng X, Shu K. Prediction of protein–protein interaction sites using convolutional neural network and improved data sets[J]. International journal of molecular sciences, 2020, 21(2): 467. [download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/Prediction%20of%20Protein%E2%80%93Protein%20Interaction%20Sites%20Using%20Convolutional%20Neural%20Network%20and%20Improved%20Data%20Sets.pdf) 8.Mahbub S, Bayzid M S. EGRET: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction[J]. bioRxiv, 2021: 2020.11. 07.372466.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/EGAT%EF%BC%9AEdge%20Aggregated%20Graph%20Attention%20Networks%20and%20Transfer%20Learning%20Improve%20Protein-Protein%20Interaction%20Site%20Prediction.pdf) 9.Chen C, Zhang Q, Yu B, et al. Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier[J]. Computers in Biology and Medicine, 2020, 123: 103899.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/Improving%20proteinprotein%20interactions%20prediction%20accuracy%20using%20XGBoost%20feature%20selection%20and%20stacked%20ensemble%20classifier.pdf) 10. Zeng M, Li M, Wu F X, et al. DeepEP: a deep learning framework for identifying essential proteins[J]. BMC bioinformatics, 2019, 20(16): 1-10.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/DeepEP%EF%BC%9Aa%20deep%20learning%20framework%20for%20identifying%20essential%20proteins.pdf) 11.Deng A, Zhang H, Wang W, et al. Developing computational model to predict protein-protein interaction sites based on the XGBoost algorithm[J]. International journal of molecular sciences, 2020, 21(7): 2274.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/Developing%20Computational%20Model%20to%20Predict%20Protein-Protein%20Interaction%20Sites%20Based%20on%20the%20XGBoost%20Algorithm.pdf) 12.Zeng M, Zhang F, Wu F X, et al. Protein–protein interaction site prediction through combining local and global features with deep neural networks[J]. Bioinformatics, 2020, 36(4): 1114-1120.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/Protein-protein%20interaction%20site%20prediction%20through%20combining%20local%20and%20global%20features%20with%20deep%20neural%20networks.pdf) 13.Li Y, Golding G B, Ilie L. DELPHI: accurate deep ensemble model for protein interaction sites prediction[J]. Bioinformatics, 2021, 37(7): 896-904.[download](https://gitee.com/fartancy/paper_repository/raw/master/protein-protein%20interaction/DELPHI%EF%BC%9Aaccurate%20deep%20ensemble%20model%20for%20protein%20interaction%20sites%20prediction%20.pdf) 14. # deep learning. # dimension reduction 1.Saul L K. A tractable latent variable model for nonlinear dimensionality reduction[J]. Proceedings of the National Academy of Sciences, 2020, 117(27): 15403-15408.[download](https://gitee.com/fartancy/paper_repository/raw/master/dimension%20reduction/A%20tractable%20latent%20variable%20model%20for%20nonlinear%20%20dimensionality%20reduction.pdf) 2.Wang Q, Qin Z, Nie F, et al. C2DNDA: A deep framework for nonlinear dimensionality reduction[J]. IEEE Transactions on Industrial Electronics, 2020, 68(2): 1684-1694.[download](https://gitee.com/fartancy/paper_repository/raw/master/dimension%20reduction/C2DNDA%20%20A%20Deep%20Framework%20for%20Nonlinear%20Dimensionality%20Reduction.pdf)