工学博士/研究员，博士生导师，国家自然科学基金项目评议专家，广西科技项目评议专家，中国人工智能学会生物信息学与人工生命专委会委员/模式识别专委会委员，中国计算机学会生物信息学专委会委员，中国图象图形学学会视觉大数据专委会委员，中国自动化学会模式识别与机器智能专委会委员/智能健康与生物信息专委会委员，国际模式识别协会（IAPR）会员。毕业于上海交通大学自动化系。研究方向为：人工智能、数据分析、机器学习、模式识别、生物信息学。主持国家自然科学基金项目3项，广西自然科学基金项目1项，其他项目若干项。担任多个国际期刊审稿人。在国际期刊发表论文多篇，均被SCI检索。欢迎态度好、编程好、英语好的同学报考！

2020级研究生，孙贵聪，获得国家奖学金

2020级研究生，孙贵聪，获得“2021-2022学年智造顺德奖学金”

2019级研究生，李昌永，考上上海交通大学博士研究生

2019级研究生，徐文枫，考上华南师范大学博士研究生

2019级研究生，徐文枫，被评为“2022届学校优秀毕业生”

2019级研究生，徐文枫，获得国家奖学金

2018级研究生，王婉茹，被评为“2021届广西区优秀毕业生”

2018级研究生，陈梅君，硕士论文被评为“2021年beat365体育亚洲入口优秀硕士学位论文”

2018级研究生，陈梅君，获得国家奖学金

2018级研究生，陈梅君，获得“研究生优秀学位论文培养项目”资助

2017级研究生，崔娟，硕士论文被评为“2020年beat365体育亚洲入口优秀硕士学位论文”

2017级研究生，朱庆祺，被评为“2020届学校优秀研究生毕业生”

2017级研究生，徐海波，获得“硕士研究生创新项目”资助

2016级研究生，吕成伟，获得“硕士研究生创新项目”资助

本科生课程《机器学习》(48学时)，《数字逻辑》（48学时）

硕士研究生课程《统计学习》(48学时)

博士研究生课程《高级机器学习》（32学时）

[32] Yongxian Fan*, and Hao Gong. An improved tensor network for image classification in histopathology, The 5th Chinese Conference on Pattern Recognition and Computer Vision, 2022, Accepted.

[31] Yongxian Fan*, and Binchao Peng. StackEPI: identification of cell line-specific enhancer-promoter interactions based on stacking ensemble learning, BMC Bioinformatics, 2022, https://doi.org/10.1186/s12859-022-04821-9.

[30] Yongxian Fan*, Guicong Sun, and Xiaoyong Pan*. ELMo4m6A: a contextual language embedding-based predictor for detecting RNA N6-methyladenosine sites, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, https://doi.org/10.1109/TCBB.2022.3173323.

[29] Wenfeng Xu, and Yongxian Fan*. Intrusion Detection Systems Based on Logarithmic Autoencoder and XGBoost, Security and Communication Networks, 2022, https://doi.org/10.1155/2022/9068724.

[28] Yongxian Fan*, and Wanru Wang. Using Multi-Layer Perceptron to identify origins of replication in eukaryotes via informative features, BMC Bioinformatics , 2021, 22:516, DOI:10.1186/s12859-021-04431-x.

[27] Yongxian Fan*, and Haibo Xu. Prediction of off-target Effects in CRISPR/CAS9 System by Ensemble Learning, Current Bioinformatics , 2021, 16: 9.

[26] Yongxian Fan*, Meijun Chen, and Xiaoyong Pan*. GCRFLDA: Scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field, Briefings in Bioinformatics , 2021, DOI: 10.1093/bib/bbab361.

[25] Wenfeng Xu, Yongxian Fan*, and Changyong Li. I2DS: Interpretable Intrusion Detection System using autoencoder and additive tree, Security and Communication Networks , 2021, DOI:10.1155/2021/5564354.

[24] Changyong Li, Yongxian Fan*, and Xiaodong Cai. PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation, BMC Bioinformatics , 2021, 22:14, DOI:10.1186/s12859-020-03943-2.

[23] Yongxian Fan*, Meijun Chen, and Qingqi Zhu. lncLocPred: Predicting LncRNA Subcellular Localization Using Multiple Sequence Feature Information, IEEE Access , 2020, DOI: 10.1109/ACCESS.2020.3007317.

[22] Yongxian Fan*, Meijun Chen, Qingqi Zhu, and Wanru Wang. Inferring Disease-associated Microbes Based on Multi-data Integration and Network Consistency Projection, Frontiers in Bioengineering and Biotechnology , 2020, DOI: 10.3389/fbioe.2020.00831.

[21] Qingqi Zhu, Yongxian Fan*, and Xiaoyong Pan. Fusing multiple biological networks to effectively predict miRNA-disease associations, Current Bioinformatics , 2020, DOI : 10.2174/1574893615999200715165335.

[20] Yongxian Fan*, Wanru Wang, and Qingqi Zhu. iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC, PLoS One , 2020, https://doi.org/10.1371/journal.pone.0228479.

[19] Yongxian Fan*, Juan Cui, and Qingqi Zhu. Heterogeneous graph inference based on similarity network fusion for predicting lncRNA-miRNA interaction, RSC Advances, 2020, 10: 11634-11642.

[18] Yong-xian Fan*, Yongzhen Li, Huihua Yang, and Xiaoyong Pan*. CNNPSP: Pseudouridine Sites Prediction Based on Deep Learning, IDEAL 2019. Lecture Notes in Computer Science , 2019, vol 11871, pp. 291–301, Springer, Cham.

[17] Yongxian Fan*, Qingqi Zhu, Chengwei Lv, and Xiaoyong Pan. Prediction of σ54 promoters in prokaryotes based on SVM–Adaboost, 2019 Chinese Automation Congress (CAC), 2019, pp. 4890-4895.

[16] Xiaoyong Pan, Yong-Xian Fan, Jue Jia, and Hong-Bin Shen*. Identifying RNA-binding proteins using multi-label deep learning, SCIENCE CHINA Information Sciences, 2019, 62(1): 019103, doi: 10.1007/s11432-018-9558-2.

[15] Yong-Xian Fan, Xiaoyong Pan, Yang Zhang, and Hong-Bin Shen*. LabCaS for ranking potential calpain substrate cleavage sites from amino acid sequence, In: Messer J. (eds) Calpain. Methods in Molecular Biology, 2019, 1915: 111-120. Springer, New York.

[14] 龚浩, 樊永显*. DNA4mcEL：基于核苷酸信息特征计算分析与预测DNA N4-甲基胞嘧啶位点, 中国生物化学与分子生物学报, 2019, 35(6): 633-647.

[13] 李永贞, 樊永显*, 杨辉华, KELMPSP: 基于核极限学习机的假尿苷修饰位点识别, 中国生物化学与分子生物学报, 2018, 34(7): 785-793.

[12] Xiaoyong Pan^{^}, Yong-Xian Fan^{^}, Junchi Yan, and Hong-Bin Shen*, IPMiner: Hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction, BMC Genomics, 2016, 17:582.

[11] 蔡国永, 吕瑞, 樊永显, 基于标签和因子分析的协同推荐方法, 北京邮电大学学报, 2015, 33(9): 34-38.

[10] Yong-Xian Fan and Hong-Bin Shen*, Predicting pupylation sites in prokaryotic proteins using pseudo-amino acid composition and extreme learning machine, Neurocomputing, 2014, 128: 267-272.

[9] Xiaoyong Pan*, Lin Zhu, Yong-Xian Fan, Junchi Yan, Predicting Protein-RNA Interaction Amino Acids using Random Forest Based on Submodularity Subset Selection, Computational Biology and Chemistry, 2014, 53: 324–330.

[8] 杨辉华*, 张晓凤, 樊永显*, 谢谱模, 褚小立, 基于一元线性回归的近红外光谱模型传递研究，分析化学, 2014, 42(9): 1229-1234.

[7] Yong-Xian Fan, Yang Zhang, and Hong-Bin Shen*, LabCaS: Labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields, PROTEINS: Structure, Function, and Bioinformatics, 2013, 81: 622-634.

[6] Yong-Xian Fan, Jiangning Song, Chen Xu, and Hong-Bin Shen*, Predicting protein N-terminal signal peptides using position-specific amino acid propensities and conditional random fields, Current Bioinformatics, 2013, 8: 183-192.

[5] Ya-Nan Zhang^, Dong-Jun Yu^, Shu-Sen Li, Yong-Xian Fan, Yan Huang, and Hong-Bin Shen*, Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features, BMC Bioinformatics, 2012, 13: 118.

[4] Zhi Zheng, Youying Chen, Liping Chen, Gongde Guo, Yongxian Fan, and Xiangzeng Kong*, Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides, Journal of Biomedicine and Biotechnology, 2012, Article ID 492174, 8 pages.

[3] Yong-Xian Fan, Jiangning Song, Xiangzeng Kong, and Hong-Bin Shen*, PredCSF: An integrated feature-based approach for predicting conotoxin superfamily, Protein and Peptide Letters, 2011, 18: 261-267.

[2] Jiang-Bo Yin^{^}, Yong-Xian Fan^{^}, and Hong-Bin Shen*, Conotoxin superfamily prediction using diffusion maps dimensionality reduction and subspace classifier, Current Protein and Peptide Science, 2011, 12: 580-588.

[1] 尹江波, 雷剑波, 樊永显, 沈红斌*, 基于扩散映射和dHKNN算法的芋螺毒素超家族预测, Chinese Conference on Pattern Recognition 2010 (CCPR2010), Chongqing, China, Oct 21-23, 2010.

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yongxian.fan@gmail.com

https://www.researchgate.net/profile/Yongxian_Fan

https://dasegroup.github.io