周树森，工学博士，毕业于哈尔滨工业大学。现任鲁东大学信息与电气工程学院副教授，研究生导师。主持完成国家自然科学基金项目1项，先后在Engineering Applications of Artificial Intelligence、Neurocomputing等国际期刊，以及ICPR、ICIP、ICDAR、COLING等国际学术会议上发表论文30多篇，授权国家发明专利13项，出版学术专著1部。
主要从事生物信息学、机器学习等领域的科研和教学工作。目前主要研究方向包括：
1. 单细胞基因组学：细胞注释与分群，细胞拟时序分析，细胞轨迹推断分化路径，跨模态数据融合。
2. 基因组功能预测：DNA、RNA序列数据的特征提取及功能预测，蛋白质功能预测。
3. 医疗健康大数据的挖掘与分析：通过基因检测精准预测个体以后患恶性肿瘤概率；整合病人基因信息进行个性化治疗。
热烈欢迎对机器学习算法在生物信息学等领域相关应用研究有兴趣的同学随时联系沟通！
个人主页：zhouss.cn
邮箱：zhoushusen@ldu.edu.cn
QQ: 270747473
[1] 2014.01-2016.12，国家自然科学基金（青年基金），基于深度置信网络的图像分类方法研究，主持。
[1] Xindi Yu, Shusen Zhou*, Mujun Zang, Qingjun Wang, Chanjuan Liu, Tong Liu. Parallel convolutional contrastive learning method for enzyme function prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(6), pp: 2604-2609, 2024. (SCI, IF: 4.5)
[2] Junjiang Liu, Shusen Zhou*, Jing Ma, Mujun Zang, Chanjuan Liu, Tong Liu and Qingjun Wang. Graph attention network with convolutional layer for predicting gene regulations from single-cell ribonucleic acid sequence data. Engineering Applications of Artificial Intelligence, 136, pp: 108938, 2024. (SCI, IF: 7.5)
[3] Minglie Li, Shusen Zhou*, Tong Liu, Chanjuan Liu, Mujun Zang and Qingjun Wang. TSVM: transfer support vector machine for predicting MPRA validated regulatory variants. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(3), pp: 472-479, 2024. (SCI, IF: 4.5)
[4] Zhengsen Pan, Shusen Zhou*, Tong Liu, Chanjuan Liu, Mujun Zang and Qingjun Wang. WVDL: weighted voting deep learning model for predicting RNA-protein binding sites. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(5), pp: 3322-3328, 2023. (SCI, IF: 4.5)
[5] Zhengsen Pan, Shusen Zhou*, Hailin Zou, Chanjuan Liu, Mujun Zang, Tong Liu and Qingjun Wang. MCNN: multiple convolutional neural networks for RNA-protein binding sites prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(2), pp: 1180-1187, 2023. (SCI, IF: 4.5)
[6] Zhengsen Pan, Shusen Zhou*, Hailin Zou, Chanjuan Liu, Mujun Zang, Tong Liu and Qingjun Wang. CRMSNet: a deep learning model that uses convolution and residual multi-head self-attention block to predict RBPs for RNA sequence. Proteins, 91(8) , pp: 1032-1041, 2023. (SCI, IF: 4.088)
[7] Xindi Yu, Shusen Zhou*, Hailin Zou, Qingjun Wang, Chanjuan Liu, Mujun Zang and Tong Liu. Survey of deep learning techniques for disease prediction based on omics data. Human Gene, 35, pp: 201140, 2023.
[8] Shusen Zhou*, Hailin Zou, Chanjuan Liu, Mujun Zang, Zhiwang Zhang, Jun Yue. Deep Extractive Networks for Supervised Learning. Optik, 127(20), pp:9008-9019, 2016. (SCI, IF:0.835)
[9] Shusen Zhou*, Qingcai Chen, and Xiaolong Wang. Active Semi-Supervised Learning Method with Hybrid Deep Belief Networks. PLoS ONE, 9(9), pp: e107122, 2014. (SCI, IF: 3.534)
[10] Shusen Zhou*, Qingcai Chen, and Xiaolong Wang. Fuzzy Deep Belief Networks for Semi-Supervised Sentiment Classification. Neurocomputing, 131(0), pp: 312-322, 2014. (SCI, IF: 2.005)
[11] Shusen Zhou, Qingcai Chen*, and Xiaolong Wang. Handwritten Chinese Text Editing and Recognition System. Multimedia Tools and Applications (MTA), 71(3), pp: 1363-1380, 2012. (SCI, IF: 0.617)
[12] Shusen Zhou*, Qingcai Chen, and Xiaolong Wang. Convolutional Deep Networks for Visual Data Classification. Neural Processing Letters (NEPL), 38(1), pp: 17-27, 2013. (SCI, IF: 0.75)
[13] Shusen Zhou*, Qingcai Chen, and Xiaolong Wang. Active Deep Learning Method for Semi-Supervised Sentiment Classification. Neurocomputing, 120(0), pp: 536-546, 2013. (SCI, IF: 2.005)
[14] Shusen Zhou*, Qingcai Chen and Xiaolong Wang. Iterative Deep Networks for Semi-Supervised Image Classification. ICIC Express Letters, 9(7), pp: 1877-1883, 2015. (EI)
[15] Yan Liu, Shusen Zhou, and Qingcai Chen*. Discriminative Deep Belief Networks for Visual Data Classification. Pattern Recognition (PR), 44(10-11), pp: 2287-2296, 2011. (SCI: 12050666, IF: 2.607, EI: 20112514071848)
[16] Shusen Zhou*, Qingcai Chen and Xiaolong Wang. Deep Networks for Online Handwriting Chinese Character Recognition. ICIC Express Letters, 9(6), pp: 1783-1789, 2015. (EI)
[17] Xiaoling Li*, Shusen Zhou. Deep Learning Method for Incomplete Data Classification. Journal of Computational Information Systems, 11(20), pp:1-8, 2015. (EI)
[18] Shusen Zhou*, Qingcai Chen, Xiaolong Wang, et al. A Novel Algorithm for Online Handwriting Chinese Document Recognition. ICIC Express Letters, 5(11), pp: 4245-4250, 2011. (EI: 20114314458315)
[1] Shusen Zhou*, Qingcai Chen, and XiaolongWang, et al. Hybrid Deep Belief Networks for Semi-supervised Sentiment Classification. International Conference on Computational Linguistics (COLING), August 23-29, pp: 1341-1349, Dublin, Ireland, 2014. (EI)
[2] Shusen Zhou*, Qingcai Chen, Xiaolong Wang, et al. An Empirical Evaluation on Online Chinese Handwriting Databases. International Workshop on Document Analysis Systems (DAS), March 27-29, pp: 455-459, Gold Coast, QLD, Australia, 2012. (EI: 20122115054612)
[3] Shusen Zhou*, Qingcai Chen, Xiaolong Wang, et al. An Empirical Evaluation on HIT-OR3C Database. International Conference on Document Analysis and Recognition (ICDAR), September 18-21, pp: 1150-1154, Beijing, China, 2011. (EI: 20114814571145, ISTP: 12345352)
[4] Shusen Zhou*, Qingcai Chen, and Xiaolong Wang. Discriminate Deep Belief Networks for Image Classification. International Conference on Image Processing (ICIP), September 26-29, pp: 1561-1564, Hong Kong, China, 2010. (EI: 20110213574279)
[5] Shusen Zhou*, Qingcai Chen, and Xiaolong Wang. Deep Quantum Networks for Classification. International Conference on Pattern Recognition (ICPR), August 23-26, pp: 2885-2888, Istanbul, Turkey, 2010. (EI: 20104613390154, ISTP: 11578187)
[6] Shusen Zhou*, Qingcai Chen, and XiaolongWang. HIT-OR3C: An Opening Recognition Corpus for Chinese Characters. International Workshop on Document Analysis Systems (DAS), June 9-11, pp: 223-230, Boston, MA, USA, 2010. (EI: 20103113108286)
[7] Shusen Zhou*, Qingcai Chen, and XiaolongWang. Active Deep Networks for Semi-Supervised Sentiment Classification. International Conference on Computational Linguistics (COLING), August 23-27, pp: 1515-1523, Beijing, China, 2010. (EI: 20114014399497)
[8] Shusen Zhou*, Qingcai Chen, and XiaolongWang. Deep Adaptive Networks for Image Classification. International Conference on Internet Multimedia Computing and Service (ICIMCS), December 30-31, pp: 61-64, Harbin, China, 2010. (EI: 20111113748644)
[9] Shusen Zhou*, Qingcai Chen, Dandan Wang, et al. A Corpus-Based Concatenative Mandarin Singing Voice Synthesis System. International Conference on Machine Learning and Cybernetics (ICMLC), July 12-15, pp: 2695-2699, Kunming, China, 2008. (EI: 20085211817258, ISTP: BII01)
[10] Qingcai Chen*, Shusen Zhou, Dandan Wang, et al. Adaptive Filter Based Prosody Modification Approach. Conference of the International Speech Communication Association (INTERSPEECH), September 22-26, pp: 789-792, Brisbane, Australia, 2008. (EI: 20104813419404, ISTP: BOM81)
[1] 韩聪聪, 周树森, 王庆军, 臧睦君, 刘通, 柳婵娟. 一种基于异质图扩散卷积网络的癌症驱动基因识别方法 [P], 中国专利: ZL202410693066.7, 2024-08-16.
[2] 刘峻江, 周树森, 柳婵娟, 王庆军, 臧睦君, 刘通. 一种基于基因突变数据的癌症转移相关基因预测方法 [P], 中国专利: ZL202410373339.X, 2024-06-14.
[3] 李锦龙, 周树森, 刘通, 柳婵娟, 王庆军, 臧睦君. 一种基于深度对比学习的激酶药物相互作用预测方法 [P], 中国专利: ZL202410313774.3, 2024-05-31.
[4] 李铭烈, 周树森, 王庆军, 臧睦君, 刘通, 柳婵娟. 一种基于深度迁移学习的调控变异预测方法 [P], 中国专利: ZL202410233955.5, 2024-04-26.
[5] 刘峻江, 周树森, 臧睦君, 刘通, 柳婵娟, 王庆军. 一种基于基因类型和氨基酸序列的决定性互补区分类方法 [P], 中国专利: ZL202410240576.9, 2024-05-14.
[6] 刘峻江, 周树森, 柳婵娟, 臧睦君, 刘通, 王庆军. 一种基于多实例学习的人体免疫状态预测方法 [P], 中国专利: ZL202311361314.X, 2024-01-05.
[7] 刘峻江, 周树森, 王庆军, 臧睦君, 柳婵娟, 刘通. 一种基于多模态的 T 细胞受体序列分类方法 [P], 中国专利: ZL202311174331.2, 2023-11-28.
[8] 于新迪, 周树森, 臧睦君, 刘通, 柳婵娟, 王庆军. 一种基于深度对比学习的酶功能预测方法 [P], 中国专利: ZL202311131004.9, 2023-11-24.
[9] 周树森, 柳婵娟, 王庆军, 臧睦君, 刘通. 一种基于深度学习的 DNA 序列功能预测方法 [P], 中国专利: ZL202311075805.8, 2023-11-21.
[10] 马婧, 周树森, 王庆军, 柳婵娟, 臧睦君, 刘通. 一种基于深度学习的单细胞 RNA 序列基因调控推断方法 [P], 中国专利: ZL202311104699.1, 2023-11-07.
[11] 于新迪, 周树森, 王庆军, 臧睦君, 柳婵娟, 刘通. 一种基于机器学习的子宫内膜癌组织学等级预测方法 [P], 中国专利: ZL202310322441.2, 2023-06-16.
[12] 周树森, 邹海林, 柳婵娟, 臧睦君, 刘通. 一种基于深度神经网络的蛋白质二级结构预测方法 [P], 中国专利: ZL201910085554.9, 2023-05-23.
[13] 潘正森, 周树森, 邹海林, 柳婵娟, 臧睦君, 刘通, 王庆军. 一种基于卷积神经网络的 RNA-蛋白质结合位点预测方法 [P], 中国专利: ZL202111519617.0, 2022-4-22.
