于彬

其他情况简介：

       于彬，教授，硕士生导师，山东省高层次人才，青岛市高层次人才。青岛科技大学生物医学大数据研究团队负责人，人工智能与生物医学大数据研究中心主任，数据科学学院智能科学与技术专业学科带头人。2005年6月硕士毕业于上海大学计算数学专业，中国科学技术大学在职博士，加拿大University of Calgary访问学者。目前主要从事可解释人工智能、生成式人工智能、机器学习、数据挖掘、生物信息学、医学图像处理、计算机视觉与自然语言处理的研究。教育部学科评估专家，教育部学位中心评审专家。山东省生物信息学会秘书长。中国计算机学会生物信息学专业委员会执行委员，中国计算机学会人工智能与模式识别专业委员会委员，中国计算机学会大数据专业委员会委员，中国自动化学会智能健康与生物信息专业委员会委员，中国细胞生物学学会功能基因组信息学与系统生物学分会理事，中国生物信息学会 (筹) 生物信息学算法研究专委会常务委员，山东省人工智能学会计算智能专委会委员。国际期刊Frontiers in Bioinformatics副主编。IEEE BIBM (生物医学与生物信息学) 程序委员会委员。近年来主持国家自然科学基金面上项目 (1项)、山东省重点研发计划项目 (1项)、山东省自然科学基金面上项目 (3项)、教育部产学合作协同育人项目 (1项)、山东省高等学校科技计划项目 (2项)、山东省重点实验室开放基金 (1项)、海南省重点实验室开放基金 (1项)、青岛海尔集团研发项目 (2项)。承担国家自然科学基金项目 (2项)、国家社会科学基金项目 (1项)、山东省自然科学基金项目 (5项)、山东省优秀中青年科学家科研奖励基金计划项目 (1项)、山东省高等学校科技计划项目 (3项)、青岛市科技计划基础研究项目 (2项) 等科研项目26项。2012、2014、2017年三次获山东省高校科研成果二等奖，2008、2009、2016年三次获山东省高校科研成果三等奖。近年来在相关研究领域的主要期刊发表学术论文120余篇，参编著作两部。其中100余篇作为第一作者或通讯作者发表在SCI (包括中科院1区、2区多篇TOP期刊)、EI检索刊物上。SCI总他引数3491，H-index 34，高被引论文5篇。2022-2024连续三年入选全球前2%顶尖科学家榜单。多篇论文被哈佛大学、剑桥大学、康奈尔大学、密歇根大学、加州大学、伊利诺伊大学香槟分校、大阪大学、新加坡南洋理工大学、蒙纳士大学、新南威尔士大学、美国国立卫生研究院、欧洲分子生物学实验室、德国人工智能研究中心等世界著名高校科研机构引用。多次受邀担任Cell、PNAS、Nature Communications、IEEE Transactions on Image Processing、IEEE Transactions on Cybernetics、Information Fusion、Bioinformatics、Briefings in Bioinformatics、Knowledge-Based Systems、Expert Systems with Applications、Neural Networks、PLOS Computational Biology、Genomics, Proteomics & Bioinformatics、Applied Soft Computing、Information Processing & Management、IEEE Journal of Biomedical and Health Informatics、BMC Genomics、International Journal of Biological Sciences、Journal of Proteome Research、IEEE/ACM Transactions on Computational Biology and Bioinformatics、Engineering Applications of Artificial Intelligence、Artificial Intelligence Review、iScience、Journal of Cheminformatics、Neural Computing and Applications、Computers in Biology and Medicine、Journal of Chemical Information and Modeling、Biomedical Signal Processing and Control、Journal of Molecular Biology、Computer Methods and Programs in Biomedicine、MATCH Communications in Mathematical and in Computer Chemistry、Chemometrics and Intelligent Laboratory Systems、Journal of Biomedical Informatics、Methods、BMC Microbiology、BMC Bioinformatics、Soft Computing、Biotechnology Journal、Molecular Genetics and Genomics、Mathematical Biosciences、Neural Processing Letters、Current Bioinformatics、Journal of Molecular Graphics & Modelling、Computational Biology and Chemistry等SCI期刊审稿人。

      近年来获校教学研究一、二等奖9项。指导研究生获得山东省优秀硕士学位论文，山东省优秀成果奖，青岛科技大学第十届研究生“学术之星”，校优秀硕士毕业论文。指导本科生参加国家大学生创新计划项目1项及中国科学院大学生创新计划项目3项。指导本科生毕业论文4次获得山东省优秀学士学位论文，10次获得校级优秀毕业论文。

       团队与澳大利亚蒙纳士大学、美国俄亥俄州立大学、沙特阿卜杜拉国王科技大学、中国科学技术大学、中山大学、山东大学、中南大学、西北工业大学等著名高校建立了密切的合作关系。欢迎同学们积极报考智能科学与技术、大数据技术与工程专业研究生，加入我们团队。同时也欢迎对AI for Science、机器学习、图像处理与大数据挖掘方向感兴趣的全校各专业大二、大三、大四本科生加入我们团队。

[1]国家自然科学基金面上项目，62172248，2022/01/-2025/12，在研，主持

[2]山东省重点研发计划项目，2019GGX101001，2019/01-2020/12，已结题，主持

[3]山东省自然科学基金面上项目，ZR2021MF098，2022/01-2024/12，在研，主持

[4]山东省自然科学基金面上项目，ZR2018MC007，2018/03-2021/06，已结题，主持

[5]山东省自然科学基金面上项目，ZR2013AM007，2013/10-2016/10，已结题，主持

[6]2018年教育部第一批产学合作协同育人项目 (浪潮集团)，201801023017，2018/10-2019/10，已结题，主持

[7]山东省高等学校科技计划项目，J17KA159，2017/06-2019/12，已结题，主持

[8]山东省高等学校科技计划项目，J13LI54，2013/01-2015/12，已结题，主持

[9]海南省计算科学与应用重点实验室开放课题，JSKX202001，2021/01-2023/12，在研，主持

[10]山东省智慧矿山信息技术重点实验室开放课题，2017/10-2018/09，已结题，主持

[1]山东高等学校优秀科研成果奖自然科学类，二等奖， 2014

[2]山东高等学校科学技术奖本科科学技术类，三等奖，2016

[3]山东高等学校科学技术奖本科科学技术类，二等奖，2017

[4]山东高等学校优秀科研成果奖自然科学类，二等奖，2012

[5]山东高等学校优秀科研成果奖自然科学类，三等奖，2009

[6]山东高等学校优秀科研成果奖自然科学类，三等奖，2008

[7]山东省优秀硕士学位论文指导老师，2020

[8]山东省研究生优秀成果奖三等奖指导老师，2019

[9]山东省优秀学位论文指导老师，2017

[10]山东省优秀学位论文指导老师，2015

[11]山东省优秀学位论文指导教师，2010

[12]山东省优秀学位论文指导教师，2009

近几年代表性SCI论文，仅列举36篇 (#表示共同第一作者，*表示通讯作者)：

[1]Sheng Ren, Xingyu Liao, Farong Liu, Jie Li, Xin Gao*, Bin Yu*. Exploring the latent information in spatial transcriptomics data via multi-view graph convolutional network based on implicit contrastive learning. Advanced Science, 2025, 12(21): 2413545. (JCR 1区 TOP IF=14.3)

[2]Yifei Wang, Pengju Ding, Congjing Wang, Shiyue He, Xin Gao, Bin Yu*. RPI-GGCN: Prediction of RNA-protein interaction based on interpretability gated graph convolution neural network and co-regularized variational autoencoders. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(4): 7681-7695. (JCR 1区TOP IF=10.2 CCF B)

[3] Pengju Ding, Jianxin Wang, Shiyue He, Xin Gao, Xu Yu, Bin Yu*. DeepUTF: Locating transcription factor binding sites via interpretable dual-channel encoder-decoder structure. Pattern Recognition, 2025, 161: 111279. (JCR 1区 TOP IF=7.5 CCF B)

[4]Xinyue Wang, Long Liu, Zhuo Chen, Haiyan Wang*, Bin Yu*. Graph convolutional network based on self-attention variational autoencoder and capsule contrastive learning for aspect-based sentiment analysis. Expert Systems with Applications, 2025, 279: 127172. (JCR 1区 TOP IF=7.5 CCF C)

[5]Jianxin Wang, Pengju Ding, Yongxin Zhu, Xin Gao, Xu Yu*, Bin Yu*. MSN-DTA: A multi-scale node adaptive graph neural network for interpretable drug-target binding affinity prediction. Knowledge-Based Systems, 2025, 320: 113699. (JCR 1区 TOP IF=7.2 CCF C)

[6]Xingyu Liao*, Yanyan Li, Shuangyi Li, Long Wen, Xingyi Li*, Bin Yu*. Enhanced integration of single-cell multi-omics data using graph attention networks. ACS Synthetic Biology, 2025, 14: 931-942. (JCR 1区 IF=3.8)

[7]Dianlong An, Panpan Liu, Yan Feng, Pengju Ding, Weifeng Zhou, Bin Yu*. Dynamic weighted knowledge distillation for brain tumor segmentation. Pattern Recognition, 2024, 155: 110731. (JCR 1区 TOP IF=7.5 CCF B)

[8]Xinyu Zhang#, Yifei Wang#, Qinqin Wei, Shiyue He, Adil Salhi, Bin Yu*. DRBPPred-GAT: Accurate prediction of DNA-binding proteins and RNA-binding proteins based on graph multi-head attention network. Knowledge-Based Systems, 2024, 285: 111354. (JCR 1区 TOP IF=8.8 CCF C)

[9]Yan Feng, Yuan Cao, Dianlong An, Panpan Liu, Xingyu Liao*, Bin Yu*. DAUnet: A U-shaped network combining deep supervision and attention for brain tumor segmentation. Knowledge-Based Systems, 2024, 285: 111348. (JCR 1区 TOP IF=8.8 CCF C)

[10]Haojie Lian, Pengju Ding, Chao Yu, Xinyu Zhang, Guozhu Liu, Bin Yu*. A weighted integration method based on graph representation learning for drug repositioning. Applied Soft Computing, 2024, 161: 111763. (JCR 1区 TOP IF=7.2)

[11]Congjing Wang, Yifei Wang, Pengju Ding, Shan Li, Xu Yu, Bin Yu*. ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks. Computers in Biology and Medicine, 2024, 170: 107944. (JCR 1区 TOP IF=7.7)

[12]Yifei Wang#, Xue Wang#, Cheng Chen, Hongli Gao, Adil Salhi, Xin Gao, Bin Yu*. RPI-CapsuleGAN: Predicting RNA-protein interactions through an interpretable generative adversarial capsule network. Pattern Recognition, 2023, 141: 109626. (JCR 1区 TOP IF=8.518 CCF B)

[13]Pengju Ding, Yifei Wang, Xinyu Zhang, Xin Gao, Guozhu Liu, Bin Yu*. DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape. Briefings in Bioinformatics, 2023, 24(4): bbad231. (JCR 1区 TOP IF=13.994 CCF B)

[14] Mingxiang Zhang, Hongli Gao, Xin Liao, Baoxing Ning, Haiming Gu*, Bin Yu*. DBGRU-SE: Predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism. Briefings in Bioinformatics, 24(4): bbad184. (JCR 1区 TOP IF=13.994 CCF B)

[15]Hongli Gao#, Bin Zhang#, Long Liu, Shan Li, Xin Gao*, Bin Yu*. A universal framework for single-cell multi-omics data integration with graph convolutional networks. Briefings in Bioinformatics, 2023, 24(3): bbad081. (JCR 1区TOP IF=13.994 CCF B)

[16]Yutong Yu, Pengju Ding, Hongli Gao, Guozhu Liu, Fa Zhang*, Bin Yu*. Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction. Briefings in Bioinformatics, 2023, 24(2): bbad036. (JCR 1区 TOP IF=13.994 CCF B)

[17]Hongli Gao#, Cheng Chen#, Shuangyi Li, Congjing Wang, Weifeng Zhou, Bin Yu*. Prediction of protein-protein interactions based on ensemble residual convolutional neural network. Computers in Biology and Medicine, 2023, 152: 106471. (JCR 1区 IF=6.698)

[18]Tingting Zhang, Jihua Jia, Cheng Chen, Yaqun Zhang*, Bin Yu*. BiGRUD-SA: Protein S-sulfenylation sites prediction based on BiGRU and self-attention. Computers in Biology and Medicine, 2023, 163: 107145. (SCI, 1区 IF=6.698)

[19]Minghui Wang, Lu Yan, Jihua Jia, Jiali Lai, Hongyan Zhou*, Bin Yu*. DE-MHAIPs: Identification of SARS-CoV-2 phosphorylation sites based on differential evolution multi-feature learning and multi-head attention mechanism. Computers in Biology and Medicine, 2023, 160: 106935. (JCR 1区 IF=6.698)

[20]Xiaolin Wang#, Hongli Gao#, Ren Qi, Ruiqing Zheng, Xin Gao*, Bin Yu*. scBKAP: a clustering model for single-cell RNA-Seq data based on bisecting K-means. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, 20(3): 2007- 2015. (JCR 1区 IF=3.702 CCF B)

[21]Yushuang Liu, Shuping Jin, Hongli Gao, Xue Wang, Congjing Wang, Weifeng Zhou, Bin Yu*. Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier. Bioinformatics, 2022, 38(5): 1223-1230 (JCR 1区 TOP IF=6.931 CCF B)

[22]Qinqin Wei#, Qingmei Zhang#, Hongli Gao, Tao Song, Adil Salhi, Bin Yu*. DEEPStack-RBP: Accurate identification of RNA-binding proteins based on autoencoder feature selection and deep stacking ensemble classifier. Knowledge-Based Systems, 2022, 256: 109875. (JCR 1区 TOP IF=8.139 CCF C)

[23]Minghui Wang#, Lili Song#, Yaqun Zhang, Hongli Gao, Lu Yan, Bin Yu*. Malsite-Deep：Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy. Knowledge-Based Systems, 2022, 240: 108191 (JCR 1区 TOP IF=8.139 CCF C)

[24]Bin Yu*, Xue Wang, Yaqun Zhang, Hongli Gao, Yifei Wang, Yushuang Liu, Xin Gao*. RPI-MDLStack: Predicting RNA-protein interactions through deep learning with stacking strategy and LASSO. Applied Soft Computing, 2022, 120: 108676 (JCR 1区 TOP IF=8.263)

[25]Qi Zhang#, Yandan Zhang#, Shan Li, Yu Han, Shuping Jin, Haiming Gu, Bin Yu*. Accurate prediction of multi-label protein subcellular localization through multi-view feature learning with RBRL classifier. Briefings in Bioinformatics, 2021, 22(5): bbab012. (JCR 1区 TOP IF=11.622 CCF B)

[26]Bin Yu*, Chen Chen, Ren Qi, Ruiqing Zheng, Patrick J. Lawrence, Xiaolin Wang, Anjun Ma, Haiming Gu. scGMAI: a Gaussian mixture model for clustering single-cell RNA-seq data based on deep autoencoder. Briefings in Bioinformatics, 2021, 22(4): bbaa316. (JCR 1区 TOP IF=11.622 CCF B)

[27]Bin Yu#,*, Cheng Chen#, Xiaolin Wang, Zhaomin Yu, Anjun Ma, Bingqiang Liu. Prediction of protein-protein interactions based on elastic net and deep forest. Expert Systems with Applications, 2021, 176: 114876. (JCR 1区 TOP IF=6.954 CCF C)

[28]Qingmei Zhang#, Peishun Liu#, Xue Wang, Yaqun Zhang, Yu Han, Bin Yu*. StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacking ensemble classifier. Applied Soft Computing, 2021, 99: 106921. (JCR 1区 TOP IF=6.725)

[29]Yan Zhang #, Yao Lu#, Wankun Chen, Yankang Chang, Haiming Gu, Bin Yu*. MSMANet: A multi-scale mesh aggregation network for brain tumor segmentation. Applied Soft Computing, 2021, 110: 107733. (JCR 1区 TOP IF=6.725)

[30]Cheng Chen#, Han Shi#, Zhiwen Jiang, Adil Salhi, Ruixin Chen, Xuefeng Cui, Bin Yu*. DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network. Computers in Biology and Medicine, 2021, 136: 104676. (JCR 1区 IF=4.589)

[31]Xue Wang#, Yaqun Zhang#, Bin Yu*, Adil Salhi, Ruixin Chen, Lin Wang, Zengfeng Liu. Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis. Computers in Biology and Medicine, 2021, 134: 104516. (JCR 1区 IF=4.589)

[32]Bin Yu*, Wenying Qiu, Cheng Chen, Anjun Ma, Jing Jiang, Hongyan Zhou, Qin Ma*. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics, 2020, 36(4): 1074-1081. (JCR 1 区 TOP IF=6.937 CCF B)

[33]Bin Yu*, Cheng Chen, Hongyan Zhou, Bingqiang Liu, Qin Ma*. GTB-PPI: predict protein–protein interactions based on L1-regularized logistic regression and gradient tree boosting. Genomics, Proteomics & Bioinformatics, 2020, 18(5): 582-592. (JCR 1区 TOP IF=7.691)

[34]Xiaoying Wang#, Bin Yu#,*, Anjun Ma, Cheng Chen, Bingqiang Liu, Qin Ma*. Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique. Bioinformatics, 2019, 35(14): 2395-2402. (JCR 1 区 TOP IF=5.610 CCF B)

[35]Jianying Lin, Hui Chen, Shan Li, Yushuang Liu, Xuan Li, Bin Yu*. Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier. Artificial Intelligence In Medicine, 2019, 98: 35-47. (JCR 1 区 IF=4.383)

[36]Bin Yu#,*, Shan Li#, Wenying Qiu, Minghui Wang, Junwei Du, Yusen Zhang, Xing Chen. Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction. BMC Genomics, 2018, 19: 478. (JCR 1 区 TOP IF=3.501)

[1]基于深度森林的蛋白质-蛋白质相互作用预测方法.中国，ZL202010021475.4，第1完成人

[2]基于信息融合和深度学习的原核生物乙酰化位点预测方法.中国，ZL201911363577.8，第1完成人

[3]基于stacking集成的RNA中N6-甲基腺苷修饰位点预测方法.中国，ZL202010021486.2，第1完成人

[1]研究生课程，生物计算，32学时

[2]本科生课程，数据科学导论，32学时