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博士生导师

梁艳春

基本情况

姓名: 梁艳春
性别:
职务:
职称: 教授
是否博导:
最高学历: 研究生
最高学位: 博士
电话: 18686604031
Email: ycliang@jlu.edu.cn

详细情况

所在学科专业: 计算机应用技术
所研究方向: 机器学习,生物信息学
讲授课程: 人工神经网络
机器学习
进化计算
计算智能
教育经历: 1974-1977 吉林大学数学系,本科生
1979-1982 吉林大学数学系,硕士生(导师:胡守信教授)
1994-1997 吉林大学数学系,博士生(导师:周钦德教授)
工作经历: 1977-1984 吉林大学数学系,助教
1984-1990 吉林大学数学系,讲师
1990-1998 吉林大学数学系,副教授
1998-1999 吉林大学数学系,教授
1999-2001 吉林大学数学系,教授、博士生导师
2001-2020 吉林大学mgm美高梅79906|主頁(歡迎您),教授、博士生导师
 
1990.10-1992.02 英国曼彻斯特大学(访问学者)
2000.01-2001.06 新加坡国立大学(客座教授)
2002-2004 新加坡高性能计算研究所(客座教授)
2006-2010 意大利特伦托大学信息工程与计算机科学系(客座教授)
2011-2019 美国密苏里大学计算机科学系(客座教授)
科研项目:

承担国家自然科学基金项目、国家高技术研究发展计划(“863”计划)项目、欧盟合作项目、教育部高等学校博士学科点专项科研基金等科研项目30余项。

承担的主要科研项目:
 [1] 国家自然科学基金面上项目:基于深度学习的个性化基因组与药物相互作用研究(61972174), 2020 -2023.
 [2] 国家自然科学基金面上项目:全基因组重测序数据高维SNP相互作用研究(61472158), 2015-2018.
 [3] 国家自然科学基金面上项目:基于农作物高通量表达谱数据的特征选择与分子网络构建的评估算法(61272207), 2013-2016.
 [4] 国家自然科学基金面上项目:农艺性状及相关基因关联网络若干基础问题研究(61073075), 2011-2013.
[5] 国家自然科学基金面上项目:极端状况下的结构振动智能控制方法(10872077), 2009-2011.
 [6] 国家自然科学基金面上项目:微机电系统动态建模与若干基础问题研究(60673023), 2007-2009.
 [7] 国家自然科学基金重点项目:生物信息学中的相关组合理论和算法研究(60433020), 2005-2008.
 [8] 国家自然科学基金面上项目:基于计算智能的振动系统仿真建模研究(19872027), 1999-2001.
[9] 国家高技术研究发展计划(863计划)项目:主要农作物基因组可协同功能注释与分析软件平台(2009AA02Z307), 2009-2011.
 [10] 高等学校博士学科点专项科研基金项目:开放存取资源的数据挖掘及文献网络结构技术(20120061110094), 2013-2013.
 [11] 吉林省科技厅国际合作项目:结肠癌与食道癌早期诊断的尿液中蛋白标志物预测(20120730), 2012-2014.
 [12] 吉林省科技厅国际合作项目:电网故障诊断智能化方法研究与应用(20080708), 2008-2010.

主持的欧盟合作项目:
[1] 欧盟合作项目: ERASMUS+ KA107 International Credit Mobility (ICM) Project, 2019-2022.
[2] 欧盟合作项目: Erasmus+ International Credit Mobility Project,2015-2017.
[3] 欧盟合作项目: Swap and Transfer (SAT, 2013-2537/001-001-EMA2), 2013-2017.
[4] 欧盟合作项目: One More Step (OMS, EM Action 2-2011-2581),2011-2015.
[5] 欧盟合作项目: Bridging the Gap (BTG, 155776-EM-1-2009-1-IT-ECW-L12),2009-2013.

学术论文:

在 IEEE TKDE, IEEE TSMC, IEEE TGRS, Information Sciences, BMC Bioinformatics, Bioinformatics, 《计算机学报》和《软件学报》等国内外重要期刊和学术会议上发表学术论文400余篇,其中 SCI 检索论文 200 余篇,EI 检索论文 200 余篇。

主要学术论文:
[1] Du Wei, Sun Yu, Bao Huimin, Chen Liang, Li Ying, Liang Yanchun. DeepHBSP: A deep learning framework for predicting human blood-secretory proteins using transfer learning. Journal of Computer Science and Technology, 2021, 36(2): 234–247.
[2] Shi Xiaohu, Guo Hongyan, Wu Chunguo, Liang Yanchun, Chang Zhiyong. A block-encoding method for evolving neural network architecture, International Journal of Bio-Inspired Computation, 2021, 18 (1): 27-37.
[3] Wu Jingqiao, Feng Xiaoyue, Guan Renchu, Liang Yanchun. Cancer Research Trend Analysis Based on Fusion Feature Representation, Entropy 2021, 23(3), 338.
[4] Cao Lixian, Liang Yanchun, Lv Wei, Park Kaechang, Miura Yasuhiro, Shinomiya Yuki, Yoshida Shinichi. Relating brain structure images to personality characteristics using 3D convolution neural network, CAAI Transactions on Intelligence Technology, 2021, 6(3): 338-346.
[5] 孟子尧,谷雪,梁艳春,许东,吴春国,深度神经架构搜索综述,计算机研究与发展,2021, 58(1): 22-33.
[6] Song Jiazhi, Liu Guixia, Jiang Jingqing, Zhang Ping, Liang Yanchun. Prediction of Protein–ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm, International Journal of Molecular Sciences, 2021, 22(2), 939.
[7] Gao Rui, Li Shoufeng, Shi Xiaohu, Liang Yanchun, Xu Dong. Overlapping Community Detection Based on Membership Degree Propagation, Entropy 2021, 23(1), 15.
[8] Yang Chen, Zhao Haishi, Bruzzone Lorenzo, Benediktsson Jon Atli, Liang Yanchun, Liu Bin, Zeng Xingguo, Guan Renchu, Li Chunlai, Ouyang Ziyuan. Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning, Nature Communications, 2020, 11: 6358.
[9] Liu Hongtao, Liang Yanchun, Wang Liupu, Feng Xiaoyue, Guan Renchu. BioNMT: A Biomedical Neural Machine Translation System, International Journal of Computers Communications & Control, 2020, 15(6), 3988.
[10] Feng Shiyao, Liang Yanchun, Du Wei, Lv Wei, Li Ying. LncLocation: Efficient Subcellular Location Prediction of long non-coding RNAs Based Multi-Source Heterogeneous Features Fusion, International Journal of Molecular Sciences. 2020, 21(19), 7271.
[11] Han Xiaosong, Zhao Haiyan, Xu Hao, Yang Yun, Liang Yanchun, Xu Dong. Molecular Basis of Food Classification in Traditional Chinese Medicine, 2020. In: Zhao Y., Chen DG. (eds) Statistical Modeling in Biomedical Research. Emerging Topics in Statistics and Biostatistics. Springer, Cham.
[12] Jiang Yuexu, Liang Yanchun, Wang Duolin, Xu Dong, Joshi Trupti. A dynamic programing approach to integrate gene expression data and network information for pathway model generation, Bioinformatics, 2020, 36: 169-176.
[13] Gu Xue, Meng Ziyao, Liang Yanchun, Xu Dong, Huang Han, Han Xiaosong and Wu Chunguo, ESAE: Evolutionary Strategy-Based Architecture Evolution, Bio-inspired Computing: Theories and Applications - 14th International Conference, BIC-TA 2019, Revised Selected Papers, Communications in Computer and Information Science (CCIS), 2020, 1159, 193–208.
[14] Feng Chao, Liu Shufen, Zhang Hao, Guan Renchu, Li Dan, Zhou Fengfeng, Liang Yanchun and Feng Xiaoyue, Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study, International Journal of Molecular Sciences. 2020, 21, 2181.
[15] Song Jiazhi, Liang Yanchun, Liu Guixia, Wang Rongquan, Sun Liyan and Zhang Ping, A Novel Prediction Method for ATP-binding Sites from Protein Primary Sequences Based on Fusion of Deep Convolutional Neural Network and Ensemble Learning, IEEE Access, 2020, 8: 21485-21495.
[16] Wang Yizhang, Zhou You, Pang Wei, Liang Yanchun, Wang Shu. Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures, Tehnički vjesnik 27, 1(2020), 89-95.
[17] Li Xin, Liang Yanchun, Zhao Minghao, Wang Chong, Jiang Yu. Few-shot learning with generative adversarial networks based on WOA13 data. Computers, Materials and Continua, 2019, 60 (3): 1073-1085.
[18] Zhang Hui, Liang Yanchun, Peng Cheng, Han Siyu, Du Wei, Li Ying. Predicting IncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks, Mathematical Biosciences, 2019, 315: UNSP 108229.
[19] Lin Xixun, Liang Yanchun, Giunchiglia Fausto, Feng Xiaoyue, Guan Renchu, Relation path embedding in knowledge graphs, Neural Computing and Applications, 2019, 31(9): 5629-5639.
[20] Li Ying, He Ye, Han Siyu and Liang Yanchun. Identification and functional inference for tumor-associated long non-coding RNA, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 16(4): 1288-1301.
[21] Guan Renchu, Wen Xiaojing, Liang Yanchun, Xu Dong, He Baorun, Feng Xiaoyue, Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts, International Journal of Biological Sciences, 2019, 15(10): 2065-2074.
[22] Cao Pengfei, Yang Zhongyi, Sun Liang, Liang Yanchun, Yang Mary Qu, Guan Renchu. Image Captioning with Bidirectional Semantic Attention-Based Guiding of Long Short-Term Memory, Neural Processing Letters, 2019, 50: 103–119.
[23] Cao Benchun, Liang Yanchun, Yoshida Shinichi, Guan Renchu. Facial Expression Decoding based on fMRI Brain Signal, International Journal of Computers Communications & Control, 2019, 14(4): 475-488.
[24] Wang Duolin, Liang Yanchun, Xu Dong. Capsule network for protein post-translational modification site prediction, Bioinformatics, 2019, 35 (14): 2386-2394.
[25] Zhang Hui, Liang Yanchun, Han Siyu, Peng Cheng, Li Ying, Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods. International Journal of Molecular Sciences, 2019, 20(6): 1284.
[26] Feng Xiaoyue, Zhang Hao, Ren Yijie, Shang Penghui, Zhu Yi, Liang Yanchun,Guan Renchu, Xu Dong, The Deep Learning-Based Recommender System "Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study, Journal of Medical Internet Research, 2019, 21{5): e12957.
[27] Jiang Mingyang, Liang Yanchun, Pei Zhili, Wang Xiye, Zhou Fengfeng, Wei Chengxi, Feng Xiaoyue. Diagnosis of Breast Hyperplasia and Evaluation of RuXian-I Based on Metabolomics Deep Belief Networks, International Journal of Molecular Sciences, 2019, 20, 2620.
[28] Sun Huiyan, Chen Liang, Cao Sha, Liang Yanchun, Xu Ying, Warburg Effects in Cancer and Normal Proliferating Cells: Two Tales of the Same Name. Genomics, Proteomics & Bioinformatics, 2019, 17(3): 273-286.
[29] Jiang Mingyang, Liang Yanchun, Pei Zhili, Wang Xiye, Jiang Jingqing, Wang Qinghu, Guan Renchu. Dilated Cardiomyopathy Metabolomics Data Classification Based on DAE-SVM Algorithm, Investigation Clinica, 2019, 60 (1): 27-34.
[30] Xu Guiping, Cui Quanlong, Shi Xiaohu, Ge Hongwei, Zhan Zhihui, Lee Heow Pue, Liang Yanchun, Tai Ran, Wu Chunguo, Particle swarm optimization based on dimensional learning strategy, Swarm and Evolutionary Computation, 2019, 45: 33-51.
[31] Guan Renchu, Wang Xu, Marchese Maurizio, Yang Mary Qu, Liang Yanchun, Yang Chen. Feature space learning model, Journal of Ambient Intelligence and Humanized Computing.
[32] Jia Guoyi, Wang Duolin, Xue Mengzhu, Liu Yuwei, Pei Yuchen, Yang Yingqun, Xu Jingmei, Liang Yanchun and Wang Peng. CircRNAFisher: A systematic computational approach for de novo circular RNA identification, Acta Pharmacologica Sinica, 2019, 40: 55-63
[33] Sun Huiyan, Liang Yanchun, Wang Yan, Chen Liang, Du Wei. Jiang Yuexu, Shi Xiaohu, Link Prediction Based on Extended Local Path Gain in Protein-Protein Interaction Network. Tehnicki Vjesnik, 2019, 26 (1): 177-182.
[34] Li Lu, Liang Yanchun, Li Tingting, Wu Chunguo, Zhao Guozhong, Han Xiaosong, Boost particle swarm optimization with fitness estimation, Natural Computing, 2019, 18(2): 229-247.
[35] Cui Quanlong, Tang Chuan, Xu Guiping, Wu Chunguo, Shi Xiaohu, Liang Yanchun, Chen Liang, Lee Heow Pueh, Huang Han. Surprisingly Popular Algorithm-Based Comprehensive Adaptive Topology Learning PSO. IEEE Congress on Evolutionary Computation (CEC), 2019, 2603-2610. Wellington, New Zealand.
[36] He Guannan, Liang Yanchun, Chen Yan, Yang William, Liu Jun S, Yang Mary Qu and Guan Renchu. A hotspots analysis-relation discovery representation model for revealing diabetes mellitus and obesity. BMC Systems Biology 2018, 12 (Suppl 7): 116.
[37] Han Siyu, Liang Yanchun, Ma Qin, Xu Yangyi, Zhang Yu, Du Wei, Wang Cankun, Li Ying. LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property. Briefings in Bioinformatics, bby065, 2018, 31 July.
[38] Wang Donghui, Liang Yanchun, Xu Dong, Feng Xiaoyue, Guan Renchu, A content-based recommender system for computer science publications. Knowledge-Based Systems, 2018, 157:1-9.
[39] Guan Renchu, Wang Xu, Qu Yang Mary, Zhang Yu, Zhou Fengfeng, Yang Chen, Liang Yanchun. Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways, Scientific Reports, 2018, 8: 267,
[40] Jiang Mingyang, Liang Yanchun, Feng Xiaoyue, Fan Xiaojing, Pei Zhili, Xue Yu, Guan Renchu. Text classification based on deep belief network and softmax regression. Neural Computing and Applications. 2018, 29(1):61-70.
[41] Lin Xixun, Liang Yanchun, Wang Limin, Wang Xu, Yang MaryQu, Guan Renchu, A knowledge base completion model based on path feature learning, International Journal of Computers, Communications & Control, 2018, 13(1): 71-82.
[42] Wang Duolin, Zeng Shuai, Xu Chunhui, Qiu Wangren, Liang Yanchun, Joshi Trupti, Xu Dong. MusiteDeep: a Deep-learning Framework for General and Kinase-specific Phosphorylation Site Prediction. Bioinformatics, 2017, 33(24): 3909–3916.
[43] Feng XY, Liang YC, Shi XH, Xu D, Wang X, Guan RC. Overfitting Reduction of Text Classification Based on AdaBELM. Entropy, 2017, 19(7), 330.
[44] Cui Quanlong, Li Qiuying, Li Gaoyang, Li Zhengguang, Han Xiaosong, Lee Heow Pueh, Liang Yanchun, Wang Binghong, Jiang Jingqing, Wu Chunguo, Globally-optimal prediction-based adaptive mutation particle swarm optimization, Information Sciences, 2017, 418: (187-217).
[45] Meng Anning, Liang Yanchun, Xu Hao, Xing Lining, Tan Xu. Architecture and key technologies for the mission planning system of space sensor network. International Journal of Sensor Networks, 2017, 24(2): 98-109.
[46] Song Tianci, Cao Sha, Sheng Tao, Liang Sen, Du Wei, Liang Yanchun. A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer. Scientific Reports. 2017, 7, 4671
[47] Song Tianci, Wang Yan, Du Wei, Cao Sha, Tian Yuan, Liang Yanchun. The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning, Journal of Bioinformatics and Computational Biology, 2017, 15(1): 1650037.
 [48] Song Tianci, Liang Yanchun, Cao Zhongbo, Du Wei, Li Ying. Computational Analysis of Specific microRNA Biomarkers for Noninvasive Early Cancer Detection. BioMed Research International. 2017, 4680650
[49] Du Wei, Cao Zhongbo, Song Tianci, Li Ying, Liang Yanchun. A feature selection method based on multiple kernel learning with expression profiles of different types, Biodata Mining, 2017, 10(4)
[50] Wang Duolin, Wang Juexin, Jiang Yuexu, Liang Yanchun, Xu Dong. BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis, Journal of Molecular Biology, 2017 429(3): 446-453.
[51] Li Ying, Shi Xiaohu, Liang Yanchun, Xie Juan, Zhang Yu, Ma Qin. RNA-TVcurve: A Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation. BMC Bioinformatics, v 18, n 1, January 21, 2017.
 [52] Han SY, Liang YC, Li Y, Du W. Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination, Biomed Research International, 2016, 8496165
 [53] Han Siyu, Liang Yanchun, Li Ying, Du Wei. Lncident: A Tool for Rapid Identification of Long Noncoding RNAs Utilizing Sequence Intrinsic Composition and Open Reading Frame Information, International Journal of Genomics, Volume 2016, Article ID 9185496
[54] Wu Tong, Liang Yanchun, Varela Ramiro, Wu Chunguo, Zhao Guozhong, Han Xiaosong. Self-adaptive SVDD integrated with AP clustering for one-class classification. Pattern Recognition Letters, 2016, 84: 232-238.
[55] Yang Chen, Bruzzone Lorenzo, Zhao Haishi, Liang Yanchun, Guan Renchu. Decorrelation-Separability-Based Affinity Propagation for Semisupervised Clustering of Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(2): 568-582.
[56] Ma Deyin, Han Xuming, Liang Yanchun, Shi Xiaohu. Fuzzy immune GA scheduling framework for JSSP. International Journal of Grid and Distributed Computing, 2016, 9(8): p 357-370.
[57] Ge Hongwei, Sun Liang, Chen Xin, Liang Yanchun. An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling. International Journal of Computational Intelligence Systems, 2016, 9(5): 917-931.
[58] Li HB, Liang YC, Zhang N, Guo JS, Xu D, Li ZS. Improving degree-based variable ordering heuristics for solving constraint satisfaction problems, Journal of Heuristics, 2016, 22(2): 125-145.
[59] Yang C, Bruzzone L, Zhao HS, Liang YC, Guan RC. Decorrelation-Separability-Based Affinity Propagation for Semisupervised Clustering of Hyperspectral Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(2): 568-582.
[60] Wang JX, Joshi T, Valliyodan B, Shi HY, Liang YC, Nguyen HT, Zhang J, Xu D. A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies, BMC Genomics, 2015, 16, 1011.
[61] Cao Z, Wang Y, Sun Y, Du W, Liang YC. A novel filter feature selection method for paired microarray expression data analysis. International Journal of Data Mining and Bioinformatics, 2015,12(4): 363-386.
[62] Du W, Cao Z, Wang Y, Zhou F, Pang W, Chen X, Liang YC. Specific biomarkers: detection of cancer biomarkers through high-throughput transcriptomics data. Cognitive Computation, 2015, 7(6): 652-666.
[63] Chen L, Heikkinen L, Knott KE, Liang YC, Wong G. Evolutionary conservation and function of the human embryonic stem cell specific miR-302/367 cluster. Comparative Biochemistry and Physiology Part D: Genomics and Proteomics, 2015, 16 (D): 83-98.
[64] Ge H, Sun L, Yang X, Yoshida S, Liang YC. Cooperative differential evolution with fast variable interdependence learning and cross-cluster mutation. Applied Soft Computing, 2015, 36: 300-314.
[65] Jiang YX, Wang Y, Pang W, Chen L, Sun HY, Liang YC, Blanzieri E, Essential protein identification based on essential protein–protein interaction prediction by Integrated Edge Weights, Methods, 2015, 83: 51-62.
[66] Liu M, He YD, Wang JX, Lee HP, Liang YC. Hybrid intelligent algorithm and its application in geological hazard risk assessment, Neurocomputing, 2015, 149: 847-853.
[67] Han XS, Liang YC, Li ZG, Li GY, Wu XZ, Wang BH, Zhao GZ, Wu CG. An efficient gnetic algorithm for optimization problems with time-consuming fitness evaluation, International Journal of Computational Methods, 2015, 12(1): 1350106-1-24.
[68] Qian Y, Liang YC, Guan RC. Improving activated sludge classification based on imbalanced data, Journal of Hydroinformatics, 2014, 16(6): 1331-1342.
[69] Zhou Chunbao, Wang Jiaxin, Wang Yao, Liang Yanchun. Identification of phage-induced genomic islands in the 13 Streptococcus pyogenes strains using genome barcodes, International Journal of Data Mining and Bioinformatics, 2014, 10(3): 269-284.
[70] Qian Y, Liang YC, Li M, Feng GX, Shi XH. A resampling ensemble algorithm for classification of imbalance problems, Neurocomputing, 2014, 143 (SI): (57-67).
[71] Li L, Wang DL, Xue MZ, Mi XQ, Liang YC, Wang P. 3 ' UTR shortening identifies high-risk cancers with targeted dysregulation of the ceRNA network. Scientific Reports, 2014, 4(5406).
[72] Sun L, Ge HW, Yoshida S, Liang YC, Tan GZ. Support vector description of clusters for content-based image annotation, Pattern Recognition, 2014, 47(3): 1361-1374.
[73] Du W, Sun Y, Wang Y, Cao ZB, Zhang C, Liang YC. A novel multi-stage feature selection method for microarray expression data analysis, International Journal of Data Mining and Bioinformatics, 2013, 7(1): 58-77.
[74] Yang C, Bruzzone L, Guan RC, Lu, LJ, Liang YC. Incremental and decremental affinity propagation for semisupervised clustering in multispectral images, IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1666-1679.
[75] Sun XL, Dong BQ, Yin LJ, Zhang RZ, Du W, Liu DF, Shi N, Li AL, Liang YC and Mao L, PMTED: a plant microRNA target expression database. BMC Bioinformatics, 2013, 14: 174.
[76] Wang JX, Chen L, Wang Y, Zhang JF, Liang YC, Xu D. A Computational Systems Biology Study for Understanding Salt Tolerance Mechanism in Rice. PLoS ONE. 2013, 8(6): e64929.
[77] Ma DY, Liang YC, Zhao XS, Guan RC and Shi XH. Multi-BPexpert system for fault diagnosis of power system. Engineering Application of Artificial Intelligence, 2013, 26(3): 937-944.
[78] Li Y, Duan M, Liang YC, Multi-scale RNA comparison based on RNA triple vector curve representation, BMC Bioinformatics 2012, 13: 280.
[79] Guo XC, Wu CG, Marchese M, Liang YC, LS-SVR-based solving Volterra integral equations, Applied Mathematics and Computation, 2012 (218): 11404-11409.
[80] Wang Y, Sun G, Ji ZH, Xing C, Liang YC. Weighted change-point method for detecting differential gene expression in breast cancer microarray data, PLoS ONE, 2012, 7(1): e29860.
[81] Wang LP, Wang JX, Wang M, Li Y, Liang YC, Xu D, A comparative study on using Internet search engines to obtain medical information, Journal of Medical Internet Research, 2012, 14(3), e74.
[82] Sun L, Yoshida S, Cheng XC, Liang YC. A cooperative particle swarm optimizer with statistical variable interdependence learning, Information Sciences. 2012, 186(1): 20-39.
[83] Liang YC, Zhang F, Wang JX, Joshi T, Wang Y, Xu D. Prediction of drought-resistant genes in Arabidopsis thaliana using SVM-RFE. PLoS ONE, 2011, 6(7): e21750.
[84] Wang LK, Wang XW, Wang Xi, Liang YC, Zhang XG. Observations on novel splice junctions from RNA sequencing data, Biochemical and Biophysical Research Communications, 2011, 409: 209-333.
[85] Wang Y, Wu CG, Ji ZH, Wang BH, Liang YC. Non-parametric change-point method for differential gene expression detection, PLoS ONE, 2011, 6(5): e20060.
[86] Guan RC, Shi XH, Marchese M, Yang C, Liang YC. Text clustering with seeds affinity propagation, IEEE Transactions on Knowledge and Data Engineering, 2011, 23(4), 627-637.
[87] Yang C, Bruzzone L, Sun FY, Lu LJ, Guan RC, Liang YC. A fuzzy-statistics-based affinity propagation technique for clustering in multispectral images, IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(6): 2647-2659.
[88] Zhao DY, Wang Y, Luo D, Shi XH, Wang LP, Xu D, Yu J, Liang YC. PMirP: A pre-microRNA prediction method based on structure-sequence hybrid features, Artificial Intelligence in Medicine, 2010, 49(2): 127-132.
[89] Zhang C, Wu CG, Blanzieri E, Zhou Y, Wang Y, Du W, Liang YC. Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model, Bioinformatics, 2009, 25 (20): 2708-2714.
[90] Yang C, Lu LJ, Lin HP, Guan RC, Shi XH, and Liang YC. A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display, IEEE Transactions on Geoscience and Remote Sensing, 2008, 46 (11): 3937-3947.
[91] Guo XC, Yang JH, Wu CG, Wang CY, and Liang YC. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization. Neurocomputing, 2008, 71(16-18): 3211-3215.
[92] Ge HW, Sun L, Liang YC, Qian F. An effective PSO-and-AIS-based hybrid intelligent algorithm for job-shop scheduling, IEEE Transactions on System, Man and Cybernetics, Part A, Systems and Humans, 2008, 38(2):358-368.
[93] Ge HW, Liang YC, Marchese M. A modified particle swarm optimization-based dynamic recurrent neural network for identifying and controlling nonlinear systems, Computers & Structures, 2007, 85(21-22): 1611-1622.
[94] Wang SQ, Wang Y, Du W, Sun FX, Wang XM, Zhou CG and Liang YC. A multi-approaches-guided genetic algorithm with application to operon prediction, Artificial Intelligence in Medicine, 2007, 41(2): 151-159.
[95] Shi XH, Liang YC, Lee HP, Lu C and Wang QX, Particle swarm optimization-based algorithms for TSP and generalized TSP, Information Processing Letters, 2007 (103): 169-176.
[96] Shi XH, Liang YC, Lee HP, Lu C and Wang LM. An improved GA and a novel PSO-GA-based hybrid algorithm. Information Processing Letters, 2005, 93 (5): 255-261.
[97] Shi XH, Liang YC, Lee HP, Lin WZ, Xu X, Lim SP. Improved Elman networks and applications for controlling ultrasonic motors. Applied Artificial Intelligence, 2004, 18 (7): 603-629.
[98] Wu CG, Liang YC, Lee HP and Lu C. A generalized chromosome genetic algorithm for generalized traveling salesman problems and its applications for machining. Physical Review E, 2004, (70): 016701-1-13.
[99] Liang YC, Feng DP, Liu GR, Yang XW and Han X. Neural identification of rock parameters using fuzzy adaptive learning parameters. Computers & Structures, 2003, 81(24-25): 2373-2382.
[100] Xu X, Liang YC, Lee HP, Lin WZ, Lim SP, Lee KH and Shi XH. Mechanical modeling of a longitudinal oscillation ultrasonic motor and temperature effect analysis. Smart Materials and Structures. 2003, 12(4): 514-523.
[101] Xu X, Liang YC, Lee HP, Lin WZ, Lim SP, Lee KH and Shi XH. Identification and speed control of ultrasonic motors based on neural networks. Journal of Micromechanics and Microengineering. 2003, Vol.13, No. 1, p.104-114.
[102] Liang YC, Lin WZ, Lee HP, Lim SP, Lee KH and Sun H, Proper orthogonal decomposition and its application - Part II: Model reduction for MEMS dynamical analysis. Journal of Sound and Vibration, 2002, Vol. 256, No. 3, p. 515-532.
[103] Liang YC, Lee HP, Lim SP, Lin WZ, Lee KH and Wu CG. Proper orthogonal decomposition and its applications - Part I: theory. Journal of Sound and Vibration, 2002, Vol. 252, No. 3, p.527-544.
[104] Liang YC, Feng DP, Lee HP, Lim SP and Lee KH. Successive approximation training algorithm for feedforward neural networks. Neurocomputing, 2002, Vol. 42, p. 311-322.
[105] Liang YC, Lin WZ, Lee HP, Lim SP, Lee KH and Feng DP. A neural-network-based method of model reduction for dynamic simulation of MEMS. Journal of Micromechanics and Microengineering, 2001, Vol. 11, No. 3, p. 226-233.
[106] Liang YC, Zhou CG, Wang ZS, Lee HP and Lim SP. An equivalent genetic algorithm based on extended strings. Information Sciences, 2001, Vol. 138, No.1-4, p. 119-135.
[107] Liang YC, Feng DP, Cooper JE. Identification of restoring forces in nonlinear vibration systems based on improved fuzzy adaptive BP algorithm. Journal of Sound and Vibration. 2001, Vol. 242, No.1, p. 47-58.
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[109] 徐旭,梁艳春,时小虎. 延时-回归神经网络及在超声马达速度控制中的应用. 电子学报,2004, 32(11): 1918-1921.
[110] 时小虎, 梁艳春, 徐旭. 改进的Elman模型与递归反传控制神经网络. 软件学报, 2003, 14(6): 1110-1119.
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[112] 梁艳春等. 基于遗传算法的Rosenbrock函数优化问题的研究. 软件学报, 1997, 8(9): 701-708.

著作教材: [1] 梁艳春,张琛,杜伟,吴春国,曹忠波. 生物信息学中的数据挖掘方法及应用,科学出版社,2011年.
[2] 梁艳春,吴春国,时小虎,葛宏伟. 群智能优化算法理论与应用,科学出版社,2009年.
[3] 吴微,周春光,梁艳春. 智能计算 (“十一五”国家级规划教材), 高等教育出版社,2009年.
[4] 周春光, 梁艳春. 计算智能,吉林大学出版社,2001年第1版,2005年第2版,2009年第3版.
获奖情况:

一、主要的省部级奖:
[1] 梁艳春等,智能计算若干方法研究,2015年教育部自然科学奖二等奖.
[2] 梁艳春等,智能计算建模及应用研究,2007年吉林省科技进步一等奖.
[3] 梁艳春等,基于计算智能的振动系统仿真建模研究,2003年吉林省科技进步二等奖.

二、荣誉称号:
[1] 2002年享受国务院政府特殊津贴
[2] 2006年被评为吉林省第九批有突出贡献的中青年专业技术人才
[3] 2007年被评为吉林省第二批拔尖创新人才
[4] 2008年被评为第二批吉林省高级专家
[5] 2011年被评为第三批吉林省高级专家
[6] 2014-2020连续七年入选Elsevier中国高被引学者榜单

社会兼职:

[1] 中国生物信息学会(筹)理事(2018-)
[2] 澳门生物信息学会理事(2018-)
[3] 全国高等学校计算机教育研究会理事(2015-)
[4] International Journal of Computational Methods 编委(2004-)

治学格言: 锲而不舍,孜孜以求