[1] 国家自然科学基金青年项目[61803151]：药物-靶标相互作用预测及其在神经退行性疾病中的应用研究，2019.01-2021.12，26万元，已结题，主持；

[2] 湖南省自然科学基金联合基金[2023JJ50201]：基于深度学习与多组学数据融合的乳腺癌细胞通讯分析研究，2023.01-2025.12，5万元，在研，主持；

[3] 湖南省自然科学基金青年项目[2018JJ3570]：药物-靶标相互作用预测及其在药物重定位中的应用研究，2018.01-2020.12，5万元，已结题，主持；

[4] 湖南省教育厅重点项目：基于机器学习和多组学数据的结直肠癌细胞通讯分析研究，2023.12-2025.12，8万元，在研，主持；

[5] 湖南省教育厅科学研究项目[20C0636]：基于机器学习的新冠肺炎关联药物筛选研究, 2020.09-2023.09，0.8万元，已结题，主持；

[6] 湖南省教育厅优秀青年项目[14B023]：乳腺癌致病蛋白质复合物挖掘及其在药物重定位中的应用，2014.05 - 2017.6，4万元，已结题，主持；

[7] 国家自然科学基金面上项目[62072172]：基于机器学习的新冠肺炎药物重定位研究，2021.01-2024.12, 57万元，在研，第1参与人；

[8] 国家自然科学基金青年项目[61702054]：基于网络重构和多尺度模块分解的人类疾病基因预测方法研究, 2008.01-2020.12, 20万元，已结题，第1参与人；

[9] 湖南省自然科学基金面上项目[2021JJ30219]：基于机器学习的抗冠状病毒 药物筛选研究，2021.01-2023.12，5万元，在研，第1参与人；

[10] 湖南省自然科学基金青年项目[2018JJ2461]；利用大数据挖掘研究衰老、老年性疾病和抗衰老药物，2018.01-2020.12，5万元，在研，第1参与人；

[11] 湖南省教育厅一般项目[09C163]：互作网络中的蛋白质功能预测及其在疾病分析中的应用研究，2009.05-2013.12，1万元，已结题，主持；

2. 学术论文

第一作者：

[1] Lihong Peng, Feixiang Wang, Zhao Wang, Jingwei Tan, Li Huang, Xiongfei Tian, Guangyi Liu, Liqian Zhou. Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies, Briefings in Bioinformatics, 2022, 23(4): bbac234. (SCI, 中科院1区, IF: 13.994，ESI高被引)

[2] Lihong Peng, Wei Xiong, Zhao Wang, Ruya Yuan, Chendi Han, Qinghui Chen, Zejun Li, Xing Chen. CellDialog: A Computational Framework for Ligand-Receptor-Mediated Cell-Cell Communication Analysis. IEEE Journal of Biomedical and Health Informatics, 2024, 28(1):580-591. (SCI, 中科院1区, 被主编推荐为封面文章，ESI高被引)

[3] Lihong Peng, Huang L, Su Q, et al. LDA-VGHB: identifying potential lncRNA–disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine[J]. Briefings in Bioinformatics, 2024, 25(1): bbad466. (SCI, 中科院1区，ESI高被引)

[4] Lihong Peng, Tan J, Xiong W, et al. Deciphering ligand–receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data [J]. Computers in Biology and Medicine, 2023: 107137. (SCI, 中科院1区)

[5] Lihong Peng, Liu X, Yang L, et al. BINDTI: a bi-directional intention network for drug-target interaction identification based on attention mechanisms[J]. IEEE Journal of Biomedical and Health Informatics, 2024. (SCI, 中科院1区)

[6] Lihong Peng, Gao P, Xiong W, et al. Identifying potential ligand–receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis[J]. Computers in Biology and Medicine, 2024, 171: 108110. (SCI, 中科院1区)

[7] Lihong Peng, Bai Z, Liu L, et al. DTI-MvSCA: An Anti-Over-Smoothing Multi-View Framework With Negative Sample Selection for Predicting Drug-Target Interactions[J]. IEEE Journal of Biomedical and Health Informatics, 2024. (SCI, 中科院1区)

[8] Lihong Peng, He Xianzhi, et al. STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and ��-sums clustering, Computers in Biology and Medicine, 2023, 166: 107440. (SCI, 中科院1区)

[9] Lihong Peng, Ren M, Huang L, et al. GEnDDn: An lncRNA–Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network[J]. Interdisciplinary Sciences: Computational Life Sciences, 2024: 1-21. (SCI, 中科院2区)

[10] Lihong Peng, Liu X, Chen M, et al. MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism[J]. Journal of Chemical Information and Modeling, 2024, 64(16): 6684-6698. (SCI, 中科院2区)

[11] Lihong Peng, Yuan Ruya, Han Chendi, et al. CellEnBoost: A boosting-based ligand-receptor interaction identification model for cell-to-cell communication inference[J]. IEEE Transactions on NanoBioscience, 2023. (SCI, 中科院2区)

[12] Lihong Peng, Yuan Ruya, Tan Jingwei, Wang Zhao, Chen Min, and Chen Xing. Analyses of cell-to-cell communication combining a heterogeneous deep ensemble framework and scoring approaches from sing-cell RNA sequencing data. 2022 International Conference on Bioinformatics & Biomedicine (BIBM 2022), 515-522.(CCF B类会议，论文录用率：16.7%)

[13] Lihong Peng, Huang L, Lu Y, et al. Identifying possible lncRNA-disease associations based on deep learning and positive-unlabeled learning[C]//2022 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, 2022: 168-173. (CCF B类会议，论文录用率：16.7%)

[14] Lihong Peng, He X, Zhang L, et al. A deep learning-based unsupervised learning method for spatially resolved transcriptomic data analysis[C]//2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 281-286. (CCF B类会议，论文录用率：16.7%)

[15] Lihong Peng, Chang Wang, Geng Tian, Guangyi Liu, Gan Li, Yuankang Lu, Jialiang Yang, Min Chen Zejun Li. Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet[J]. Frontiers in Microbiology, 2022: 3523. (SCI, 中科院2区)

[16] Lihong Peng, Jingwei Tan, Xiongfei Tian, Liqian Zhou. EnANNDeep: An Ensemble-based lncRNA-protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models. Interdisciplinary Sciences: Computational Life Sciences, 2022, 14(1): 209-232. (SCI, 中科院2区)

[17] Lihong Peng, Bo Liao, Wen Zhu, Ze-Jun Li, Ke-Qin Li (IEEE fellow). Predicting Drug-Target Interactions with Multi-information Fusion. IEEE Journal of Biomedical and Health Informatics, 2017, 21(2): 561-572. (SCI, 中科院 1区)

[18] Lihong Peng, Chang Wang, Xiongfei Tian, Liqian Zhou, and Keqin Li. Finding lncRNA-protein Interactions Based on Deep Learning with Dual-net Neural Architecture. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021. (SCI, 中科院 2区)

[19] Lihong Peng, Ling Shen, Longjie Liao, Guangyi Liu and Liqian Zhou. RNMFMDA: A microbe-disease association identification method based on reliable negative sample selection and logistic matrix factorization with neighborhood regularization. Frontiers in microbiology, 2020. (SCI, 中科院 2区, TOP期刊)

[20] Lihong Peng, Liqian Zhou, Xing Chen, Xue Piao. A computational study of potential miRNA-disease association inference based on ensemble learning and kernel ridge regression. Frontiers in Bioengineering and Biotechnology, 2020, 8: 40. (SCI, 中科院 2区)

[21] Lihong Peng, Xiongfei Tian, Geng Tian, Junlin Xu, Xin Huang, Yanbin Weng, Jialiang Yang, Liqian Zhou. Single-cell RNA-seq clustering: datasets, models, and algorithms. RNA Biology, 2020: 1-19. (SCI, 中科院 2区)

[22] Lihong Peng, Jun Yin, Liqian Zhou, Ming-Xi Liu, Yan Zhao. Human microbe-disease association prediction based on adaptive boosting. Frontiers in microbiology, 2018, 9: 2440. (SCI, 中科院 2区)

[23] Lihong Peng, Ruya Yuan, Ling Shen, Pengfei Gao, Liqian Zhou. LPI-EnEDT: An Ensemble Framework with Extra Tree and Decision Tree Classifiers for Imbalanced lncRNA-protein Interaction Data Classification. BioData Mining, 2021, 14(1): 1-22. (SCI, 中科院 3区)

[24] Lihong Peng, Ling Shen, Xiongfei Tian, Fuxing Liu, Juanjuan Wang, Geng Tian, Jialiang Yang, Liqian Zhou. Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures. Scientific reports, 2021, 11(1): 1-11. (SCI, 中科院 3区)

[25] Lihong Peng, Xiongfei Tian, Ling Shen, Ming Kuang, Geng Tian, Jialiang Yang, Liqian Zhou*. Identifying effective antiviral treatment against SARS-CoV-2 by drug repositioning through virus-drug association prediction. Frontiers in Genetics. (SCI, 中科院 3区)

[26] Lihong Peng, Fuxing Liu, Jialiang Yang, Xiaojun Liu, Yajie Meng, Xiaojun Deng, Cheng Peng, Geng Tian, Liqian Zhou. Probing lncRNA-protein interactions: data repositories, models, and algorithms. Frontiers in genetics, 2019,10. (SCI, 中科院 3区)

[27] Lihong Peng, Chuanneng Sun, Nana Guan, Xing Chen. HNMDA: heterogeneous network-based miRNA–disease association prediction. Molecular Genetics & Genomics, 2018, 293(4): 983-995. (SCI, 中科院 3区)

[28] Lihong Peng, Bo Liao, Wen Zhu, Zejun Li. Predicting Drug-Target Interactions with Neighbor Interaction Information and Discriminative Low-rank Representation. Current Protein & Peptide Science, 2018,19(5):455-467. (SCI, 中科院 3区)

[29] Lihong Peng, Wen Zhu, Bo Liao, Min Chen, Jialiang Yang. Screening drug-target interactions with positive-unlabeled learning. Scientific Reports, 2017, 7:8087. (SCI, 中科院 3区)

[30] Lihong Peng, Yeqing Chen, Ning Ma, Xing Chen. Negative-Aware and Rating-based Recommendation algorithm for MiRNA-Disease Association prediction. Molecular BioSystems, 2017, 13(12): 2650-2659. (SCI, 中科院 3区)

通讯作者：

[1] Ling Shen, Fuxing Liu, Liqian Zhou, Lihong Peng*. VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining unbalanced bi-random walk and Laplacian regularized least squares. Computers in Biology and Medicine, 2022, 140: 105119. (SCI, 中科院 1区)

[2] Zhou Liqian, Peng Xinhuai, …, Peng Lihong*. Unveiling patterns in spatial transcriptomics data: a novel approach utilizing graph attention autoencoder and multi-scale deep subspace clustering network. GigaScience, accepted. (SCI, 中科院 2区, IF: 11.8)

[3] Liqian Zhou, Wang Zhao, Xiongfei Tian, Lihong Peng*. LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA–protein interaction identification. BMC Bioinformatics, 2021, 22(1): 1-24. (SCI, 中科院 2区)

[4] Liqian Zhou, Qi Duan, Xiongfei Tian, Jianxin Tang, Lihong Peng*. LPI-HyADBS: A Hybrid Framework for lncRNA-protein Interaction Prediction Integrating Feature Selection and Classification. BMC bioinformatics, 2021, 22(1): 1-31. (SCI, 中科院 2区)

[5] Xiongfei Tian, Ling Shen, Pengfei Gao, Li Huang, Guangyi Liu, Liqian Zhou, Lihong Peng*. Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization with Kernel Diffusion. Frontiers in microbiology, 2022, 13. (SCI, 中科院 2区)

[6] Wang F, Yang H, Wu Y, Lihong Peng*. SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM[J]. Frontiers in Microbiology, 2023, 14: 1207209. (SCI, 中科院 2区)

[7] Liqian Zhou, Juanjuan Wang, Guangyi Liu, Qingqing Lu, Ruyi Dong, Geng Tian, Jialiang Yang, Lihong Peng*. Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method. Genomics, 2020, 112(6): 4427-4434. (SCI, 中科院 2区)

[8] Xiongfei Tian, Ling Shen, Pengfei Gao, Li Huang, Guangyi Liu, Liqian Zhou, Lihong Peng*. Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion. Frontiers in microbiology, 2022, 13. (SCI, 中科院 2区, TOP期刊)

[9] Juanjuan Wang, Chang Wang, Ling Shen, Liqian Zhou, Lihong Peng*. Screening potential drugs for COVID-19 based on bound nuclear norm regularization. Frontiers in Genetics, 1710. (SCI, 中科院 3区)

[10] Xiongfei Tian, Ling Shen, Zhenwu Wang, Liqian Zhou, Lihong Peng*. A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure. Scientific Reports, 2021, 11(1): 1-15. (SCI, 中科院 3区)

[11] Fuxing Liu, Lihong Peng, Geng Tian, Jialiang Yang, Hui Chen, Qi Hu, Xiaojun Liu, Liqian Zhou. Identifying small molecules-miRNA Interactions based on credible negative sample selection and random walk. Frontiers in Bioengineering and Biotechnology, 2020, 8. (SCI, 中科院 2区)

[12] Liqian Zhou, Zejun Li, Jialiang Yang, Lihong Peng*. Revealing Drug-Target Interactions with Computational Models and Algorithms. Molecules, 2019, 24(9): 1714. (SCI, 中科院 3区)

3. SCI期刊副主编或编委

[1] Frontiers in genetics

[2] Frontiers in microbiology

[3] Current Chinese Science

[4] Interdisciplinary Sciences: Computational Life Sciences

[5] BMC Medical Informatics and Decision Making

4. 论文获奖

Lihong Peng, Xiongfei Tian, Ling Shen, Pengfei Gao, Liqian Zhou. Discovery of Potential therapeutic drugs for COVID-19 through logistic matrix factorization with kernel diffusion. 优秀论文，中国生物工程学会.

5. 专利

[1] 彭利红，王畅，周立前，王娟娟. 一种基于深度学习的双网络神经结构预测lncRNA-蛋白质相互作用方法，发明专利，ZL 2021 1 0592443.4.

[2] 彭利红，田雄飞，周立前，王娟娟. 一种基于深度森林和PU学习的药物-靶标关系预测方法，发明专利，ZL 2020 1 1423290.2．

[3] 彭利红，刘龙龙，王钊，周立前. 一种基于Boosting与深度森林的集成模型和单细胞测序技术的细胞通讯的方法 ，2022-09-30，中国，CN202211213760.1.

[4] 彭利红，白宗正，袁儒雅，周立前. 一种基于异构深度集成模型的细胞通讯预测方法，2022-09-30，中国，CN202211213872.7.

[5] 彭利红，阳龙，谭经纬，何显志. 一种分析配体-受体相互作用介导的细胞通讯方法，2022-9-30，中国，CN202111217739.9

[6] 彭利红，王飞翔，聂立波，高鹏飞. 一种基于半监督非负矩阵分解和最小角回归的空间转录组解卷积方法及应用, 2024-03-12,中国，CN 2024102812290.

[7] 彭利红，阳龙，周立前，刘鑫. 一种基于具有可靠增强与冗余减少策略的图对比学习空间转录组聚类方法, 2024，中国.

[8] 彭利红，黄亮亮，周立前，刘龙龙. 一种肿瘤细胞通信预测方法，2024，中国.

6. 软件著作权

[1] 基于深度学习的CT图像新冠肺炎检测软件，No. 06523713，软件著作权, 2020.

[2] 家庭物联网的智能管理系统，软件著作权, No. 06405924，2020.

[3] 基于集成方法的预测治疗病毒的药物软件，软件著作权, No. 06444969, 2020.

[4] 基于KATZ和多信息融合的新冠肺炎候选药物筛选软件，软件著作权, No. 06339234，2020.

[5] 基于深度学习的肺炎ct图像识别软件V1.0, 2021SR0986888, 2021-04-15.

[6] 黄亮亮;彭利红,基于深度学习的图片验证码识别系统V1.0, 2022SR0040565, 2021-10-31.

[7] 基于深度学习的ct图像新冠肺炎检测软件V1.0, 2020SR1 170764, 2020-05-30.

[8] 基于GAPK相似性与有界核范数正则化的新冠肺炎候选药物筛选软件V1.0, 2021SR1180299, 2021-06-06.

[9] 基于3d卷积神经网络的肺癌分割系统V1.0, 2021SR183 4227, 2021-10-06.

7. Reviewer

[1] Briefing in Bioinformatics, 2021-present

[2] IEEE Journal of Biomedical and Health Informatics, 2021-present

[3] Computers in Biology and Medicine, 2020-present

[4] Frontiers in Microbiology, 2020-present

[5] BMC Bioinformatics, 2020-present

[6] Genomics, 2020-present

[7] Pharmaceutics, 2020-present

[8] BioMed Research International，2018-present

[9] Theoretical Biology and Medical Modelling, 2018-present

[10] Current Protein and Peptide Science, 2016-present

[11] Journal of Chemical Information and Modeling, 2019-present

[12] IEEE Access, 2017-present

[13] Frontiers in Genetics, 2018-present

[14] Scientific Reports, 2017-present

8. 团队建设

[1] 第一届大数据背景下的数据分析和人工智能研讨会，国际会议，主持，2019年11月8日-10日

[2] 第二届大数据背景下的数据分析和人工智能研讨会，国内会议，主持，2020年11月13日-15日

[3] 生命科学与化学学院博士点建设骨干老师，医学人工智能方向，2019年至现在

9. 专业建设

[1] 湖南省普通高校一流本科专业－网络工程，省级，2020年

[2] 湖南省普通高校本科专业综合班次价A等，省级，2019年

10. 教学业绩