2024-2023

1. Han Xu,Yusen Ye,Ran Duan,Yong Gao,Yuxuan Hu,Lin Gao*. Beaconet: A reference-free method for integration of multiple batches of single-cell transcriptomic data in original molecular space. Advanced Science,2024,230667
2. Lanying Wang, Lin Gao*,Yuxuan Hu. Adjustment of scRNA-seq data to improve cell-type decomposition of spatial  transcriptomics. Briefings in Bioinformatics,2024,25(2):bbae063
3. Yuxuan Hu, Jiazhen Rong, Yafei Xu, Runzhi Xie, Jacqueline Peng,Lin Gao,Kai Tan,  Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nature Methods, 2024,21:267-278
4. Shichen Fan, Dachang Dang, Lin Gao,* et al.  ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data. Advanced Science, 2024,2406364:1~19
5. Weibing Wang; Yusen Ye; Lin Gao*. Statistical modeling and significance estimation of multi-way chromatin contacts with HyperloopFinder. Briefings in Bioinformatics, 2024,25(4):bbae341
6. Junping Li,Minghui Sun,Yusen Ye, Lin Gao*, DeCGR: an interactive tool for automatically deciphering complex genome rearrangements from chromatin contact maps  BMC Genomics ,25:1152, 11-29
7. Yahui Lei, Xiao-Tai Huang, Xingli Guo, Kei Hang Katie Chan, Lin Gao.DeepGRNCS: Deep learning-based framework for jointly inferring Gene Regulatory Networks across Cell Subpopulations. Briefings in Bioinformatics, 2024,25(4):bbae334.
8. Pengyong Li ,Kaihao Zhang, Tianxiao Liu,Ruiqiang Lu,Yangyang Chen,Xiaojun Yao,Lin Gao ,Xiangxiang Zeng. A deep learning approach for rational ligand generation with toxicity control via reactive building blocks. Nature Computational Science, 2024,4: 851-864
9. Haojie Zhai, Lanying Wang,Yusen Ye,Yuxuan Hu, Lin Gao*. Exploring Hierarchical Structures of Cell Types in Single-Cell RNA Sequencing Data. In 20th International Symposium on Bioinformatics Research and Applications(ISBRA 2024), Kunming, China, July 19–21, 2024,  pp. 1-13 Proceedings, Part II
10. Junping Li, Lin Gao*,Yusen Ye. HiSV: a a control-free method for structural variation detection from Hi-C data. PLoS  Computational Biology 2023, 19(1):e1010760 
11. Shichen Fan,Lin  Lin Gao*. scHi-CSim: a flexible simulator that generates high-fidelity single-cell Hi-C data for benchmarking. Journal of Molecular Cell Biology,2023,15(1): mjad003
12. Meng Lan, Shixiong Zhang, Lin Gao*. Efficient generation of paired single-cell multiomics profiles by deep learning. Advanced Science, 2023,2301169:1-13  
13. Xing Chen, Lin Gao*,Yuxuan Hu.RobustCCC: a robustness evaluation tool for cell-cell communication methods, Frontiers in Genetics，2023,14:1-10
14. Peizhuo Wang, XiaoWen, Han Li,Peng Lang,Shuya Li, Yipin Lei,Hantao Shu, Lin Gao, Dan Zhao,Jianyang Zeng. Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nature Communications,2023,14:8459
15. Yusen Ye, Shihua Zhang, Lin Gao, Yuqing Zhu, Jin Zhang. Deciphering Hierarchical Chromatin Domains and Preference of Genomic Position Forming Boundaries in Single Mouse Embryonic Stem Cells. Advanced Science,2023,202205162:1-13
16. Lu Chen, Liang Yu, Lin Gao. Potent antibiotic design via guided search from antibacterial activity evaluations. Bioinformatics, 2023;39(2):btad059.
17. Haojie Zhai, Lanying Wang,Yusen Ye,Yuxuan Hu, Lin Gao*. Exploring Hierarchical Structures of Cell Types in Single-Cell RNA Sequencing Data. ISBRA 2024

2022-2021

     

1. Ran Duan, Lin Gao* et al. Comparsion and Evaluation on Omics Data Integration Methods for Cancer Subtyping. PLoS  Computational Biology 2021,17(8):e1009224
2. Weibing Wang, Lin Gao *, Yusen Ye, Yong Gao. Predicting CTCF-mediated chromatin loops with transitivity.   Bioinformatics, 2021,1-8  doi: 10.1093/bioinformatics/btab534
3. Chenxing Zhang, Lin Gao*,Bingbo Wang, Yong Gao. Improving Single-Cell RNA-seq Data Clustering by IntegrPathway.    Briefings in Bioinformatics, 2021,bbab147
4. Yuxuan Hu,Lin Gao et al. CytoTalk: De novo construction of signal transduction networks using single-cell  transcriptomics  data. Science Advances, 2021,7: eabf1356
5. Xiao Wen, Lin Gao*, Yuxuan Hu et al. CeNet Omnibus: An R/Shiny Applicationto the Construction and Analysis of  Competing Endogenous RNA Network. BMC Bioinformatics,2021,22:75
6. Han Xu, Lin Gao*,Mingfeng Huang. A network embedding based method for partial multi-omics integration in  cancer subtyping. Methods,2021, 192：67-76
7. Liang Yu, Liang Yu, …, Fei Xie, Lin Gao*,Xiangzhi Li*. Predicting therapeutic drugs for hepatocellular carcinoma based  on  tissue-specific pathways.PLoS Computational Biology,2021,17(2): e1008696
8. YifanShang,Lin Gao,Quan,Liang Yu. Prediction of Drug-Target Interactions Based on Multi-layer Network Representation  Learning. Neurocomputing, 2021, 434:80-89
9. 高琳,胡宇轩,叶育森,张世雄. 单细胞数据驱动的关键问题与挑战. 中国计算机学会通讯,2022.18(4):28-35.
10. Shujie Ren, Liang Yu, Lin Gao. Multidrug Representation Learning Based on Pretraining Model and Molecular Graph for Drug Interaction and Combination Prediction. Bioinformatics，2022,38(18): 4387-4394
11. Huangxiaotai LinGao.Reconstruction of human protein-coding gene functional association network based on machine learning.Briefings in Bioinformatics,2022:bbab552
12. Liang Yu, Yujia Zheng, Lin Gao. MiRNA-Disease Association Prediction Based on Meta-Paths. Briefings in Bioinformatics, 2022, 23(2): bbab571
13. Bingbo Wang, Xiujuan Ma, Cunchi Wang, Mingjie Zhang, Qianhua Gong, Lin Gao*.Conserved Control Path in Multilayer Networks. Entropy, 2022,24(7):979

2020-2019

     [1] Yuxuan Hu, Lin Gao*, et al. Optimal control nodes in disease-perturbed networks as targets for combination                                     therapy.Nature   Communications,2019, 10:2180  IF=12.353

[2] Yusen Ye, Lin Gao*,Shihua Zhang*. CIRCLET Circular trajectory reconstruction uncovers cell-cycle progression and              regulatory dynamics from single-cell Hi-C maps.Advanced Science,2019:1900986   IF=15.804

[3] Yusen Ye, Lin Gao*,Shihua Zhang*. MSTD: an efficient method for detecting multi-scale topological domains from                  symmetric and asymmetric 3D genomic maps.Nucleic Acids Research, 2019,47(11):e65 ( IF=11.561)

[4] Ran Duan, Lin Gao* et al.CEPIM: a comparison and evaluation platform for integration methods in cancer subtyping.              Frontiers in Genetics, 2019,10:966, 0814

[5] Liang Yu, Shunyu Yao, Lin Gao*, Yunhong Zha.Conserved disease modules extracted from multil[ant]ayer heterogeneous disease and gene networks for understanding disease mechanisms and predicting disease treatments.Frontiers in Genetics2019,9:745

[6] Liang Yu, Lin Gao.Human pathway-based disease network. IEEE/ACM Transactions on Computational Biology and               Bioinformatics2019,16(4):1240~1249

     [7] Research Highlights. Discovery of synergistic key regulators for combination therapy. Science Foundation in China，      2019,27(3):15

   [8] Book chapter “MSTD for detecting topological domains from 3D genomic maps” for an edition on “Stem Cell Transcriptional Networks - Methods and Protocols, Second Edition”, Methods in Molecular Biology, published by Springer Nature.

     [9] Feng Li, Lin Gao* ,Wang Bingbo. Detection of Driver Modules with Rarely Mutated Genes in Cancers. IEEE/ACM  Trans. on Computational Biology and Bioinformatics ,2020,17(2):390-401

    [10] Xiao Wen, Lin Gao*, Yuxuan Hu. LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation. Frontiers in Genetics, 2020,11:235, 0318

     [11] Bingbo Wang, Jie Hu, Yajun Wang, Chenxing Zhang, Yuanjun Zhou, Liang Yu, Xingli Guo, Lin Gao. C3: connect separate  connected components to form a succinct disease module. BMC Bioinformatics,2020,21:433

    [12] Haiyan Jin , ChenXing Zhang , Mengzhou Maa, Qianhua Gong , Liang Yub,Xingli Guo, Lin Gao , Bingbo Wang.   Inferring essential proteins from centrality in interconnected multil[ant]ayer networks. Physica A, 2020,557:124853

2018

[1] Fei Song, Lin Gao*, Qinghua Cui*. miES: predicting the essentiality of miRNAs with machine learning and sequence features. Bioinformatics,2018:bty738

[2] Wang, Lin Gao*,Yuxuan Hu, Feng Li. Feature Related Multi-view Nonnegative Matrix Factorization for Identifying Conserved Functional Modules in Multiple Biological Networks.BMC Bioinformatics, 2018,19:394

[3] Xiao Wen, Lin Gao*, et al. LNCSLDB: a resource for long non coding RNA subcellular localization. Database, 2018,1-6 doi:10.1093/database/bay085

[4] Feng Li, Lin Gao*, Peizhuo Wang,Yuxuan Hu. Identifying cancer specific driver modules using network based method. Molecules, 2018,23,1114

[5] Xindong Zhang, Lin Gao*, Songwei Jia. Extracting fitness relationships and oncogenic patterns among driver genes in cancer. Molecules, 2018, 23, 39   SCI IF=2.861

[6] Songwei Jia, Lin Gao*,Yong Gao, et al.Viewing the Meso-Scale Structures in Protein-Protein Interaction Networks Using 2-Clubs. IEEE Access,2018,6:36780-36795

[7] Kai Shi, Lin Gao*, Bingbo Wang. Inferring dysregulated pathways of driving cancer subtypes through multi-omics integration. 14th International Symposium on Bioinformatics Research and Application(ISBRA2018), Beijing, China, June 8-11 p.101-112   

[8] Xingli Guo, Lin Gao*, Yu Wang, et al. A large-scale investigation of long noncoding RNA secondary structures in human and mouse. Current Bioinformatics, 2018,13(5)

[9] Liang Yu, Jin Zhao, Lin Gao. Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity. International Journal of Biological Sciences, 2018,14(8):971-982 DOI: doi:10.7150/ijbs.23350

[10] Liang Yu, Lin Gao.Human pathway-based disease network. IEEE/ACM Trans. on Computational Biology and Bioinformatics, DOI 10.1109/TCBB.2017.2774802

2017

[1] Xiaofei Yang, Lin Gao*, et al. Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns. Briefings in Bioinformatics, 2017,18(5):761-773

[2] Xiaotai Huang, Yuan Zhu et al. Inference of cellular level signaling networks using single-cell gene expression data in C. elegans reveals mechanisms of cell fate specification. Bioinformatics, 2017, 33(10):1528-1535

[3] Peizhuo Wang, Lin Gao*,et al. Dynamic community detection based on network structural perturbation and topological similarity. Journal of Statistical Mechanics: Theory and Experiment, 2017 : 013401

[4] Songwei Jia, Lin Gao*,Yong Gao, et al. Exploring triad-rich substructures by graph-theoretic characterizations in complex networks. Physica A , 2017,468:53-69    

[5] Yusen Ye, Lin Gao* et al. Integrative analysis of transcription factor combinatorial interactions using a Bayesian tensor factorization approach. Frontiers in Genetics, 2017,8:140

[6] Liang Yu, Jin Zhao, Lin Gao. Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome. Artificial Intelligence in Medicine,2017,77:53-63

[7] Liang Yu, Ruidan Su, Bingbo Wang, Long Zhang, Yapeng Zou, Jing Zhang, Lin Gao. Prediction of novel drugs for hepatocellular carcinoma based on multi-source random walk. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14(4):966-977.

2016

[1] Kai Shi, Lin Gao*, Bingbo Wang. Systematic tracking of coordinated differential network motifs identifies novel disease-related genes by integrating multiple data. Neurocomputing, 2016, 206:3-12【 SCI IF=2.005 中科院Ⅱ区】

[2] Xiaofei Yang, Xiaojia Shao, Lin Gao*,et al. Comparative DNA methylation analysis to decipher common and cell type-specific patterns among multiple cell types. Briefings in Functional Genomics, 2016: elw013【SCI IF= 3.67, 中科院II区】 

[3] Hao Wu, Lin Gao*,et al. Network-based method for inferring cancer progression at the pathway level from cross-sectional mutation data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016,13(6): 1306-1044   中科院Ⅲ区

[4] Fei Xiao, Lin Gao*, Yushen Ye. Inferring gene regulatory networks using conditional regulation pattern to guide candidate genes. PLoS ONE,2016, 11(5): e0154953 中科院Ⅲ区

[5] Xingli Guo, Lin Gao* et al. Advances in long noncoding RNAs: identification, structure prediction and function annotation. Briefings in Functional Genomics, 2016,15(1):38-46 【SCI IF=3.427, 中科院Ⅱ区】

[6] Feng Li, Lin Gao*. Detection of driver pathways using mutated gene network in cancer.  Molecular BioSystems 2016, DOI: 10.1039/C6MB00084C 2016 Hot Articles in Molecular BioSystems 中科院Ⅲ区

[7] Kai Shi, Lin Gao*, Bingbo Wang. Discovering potential cancer driver genes by an integrated network-based approach. Molecular BioSystems,2016, DOI: 10.1039/C6MB00274A  中科院Ⅲ区

[8] XinDong Zhang, Lin Gao*,Zhiping Liu, Luonan Chen.Uncovering driver DNA methylation patterns in non-smoking early stage lung adenocarcinoma. BioMed Research International, 2016, Article ID 2090286 中科院Ⅲ区

[9] Yue Deng, Lin Gao*. Integrating phenotypic feature and tissue-specific networks to prioritize disease genes. Science China-Information Science ,2016, 59(7): 070101中科院IV区

[10] Liang Yu , Ruidan Su, Bingbo Wang, Yapeng Zou Jing Zhang, Lin Gao. Prediction of novel drugs for hepatocellular carcinoma based on multi-source random walk. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016 .2550453 

[11] Liang Yu, Xiaoke Ma, Long Zhang, Jing Zhang, Lin Gao. Prediction of new drug indications based on clinical data and network modularity.  Scientific Reports, 2016,32530

2015

[1] Kai Shi, Lin Gao*, Bingbo Wang. Systematic tracking of coordinated differential network motifs identifies novel disease-related genes by integrating multiple data. Neurocomputing, 2016, 206:3-12【 SCI IF=2.005 中科院Ⅱ区】

[1] Xiaofei Yang, Xiaojian Shao, Lin Gao*,Shihua Zhang*. Systematic DNA methylation analysis of multiple cell lines reveals common and specific patterns within and across tissues of origin. Human Molecular Genetics,2015,24(15):4374~4384【 SCI IF= 6.677,JCR=1】

[2] Songwei Jia, Lin Gao*, Yong Gao, James Nastos, et al. Defining and identifying cograph communities in complex networks.New Journal of Physics,2015: 013044 【SCI IF=3.673,JCR=1】
[3] Hao Wu, Lin Gao*, Feng Li, Fei Song, Xiaofei Yang.Identifying Overlapping Mutated Driver Pathways by Constructing Gene Networks in Cancer. BMC Bioinformatics,2015,16(Suppl 5):S3
[4] XinDong Zhang,  Lin Gao*,Zhiping Liu, Luonan Chen. Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity . BMC Bioinformatics,2015,16:92  【 SCI IF= 2.67,JCR=2】
[5] Yue Deng,  Lin Gao*,Xiaocheng Huang, Xingli Guo, Bingbo Wang. HPOSim: an R package for phenotypic similarity measure and enrichment analysis based on the Human Phenotype Ontology.PLoS ONE, 2015,10(2):e0115692.【SCI IF=3.73,JCR=1】
[6] Xingli Guo,  Lin Gao*et al. Advances in long noncoding RNAs: identification, structure prediction and function annotation. Briefings in Functional Genomics 【SCI IF=3.427,JCR=1】
[7] Liang Yu, Jianbin Huang, Zhixin Ma, Jing Zhang, Yapeng Zou,  Lin Gao*.Inferring drug-disease associations based on known protein complexes. BMC Medical Genomics 【 SCI IF= 3.914】

[8]  Xiaoke Ma, Long Gao, Chi Fung Lee,Peng Gao, Georgios Karamanlidis, Lorena Menendez, Rong Tian and Kai Tan. Revealing pathway dynamics in heart diseases by analyzing multiple differential networks. PLoS Computational Biology,2015,11(6):e1004332. 【SCI IF=6.236】

[9] Bingbo Wang*, Lin Gao*.Qingfang zhang et al. Diversified Control Paths: A Significant Way Disease Genes Perturb the Human Regulatory Network. PLoS ONE,2015, 10(8): e0135491
 
2014
[1] Bingbo Wang, Lin Gao*,Yong Gao, Yue Deng and Yu Wang. Controllability and observability analysis for vertice domination centrality in directed networks. Scientific Reports, 2014,4:5399 【SCI IF=5.078,JCR=1】
[2] Xiaofei Yang, Lin Gao*,Xingli Guo, Hao Wu, Fei Song, Bingbo Wang.A network based method for analysis of lncRNA-disease associations and prediction of lncRNAs implicated in diseases . PLoS ONE,2014,9(1): e87797【SCI IF=3.73,JCR=1】
[3] Hao Wu,Lin Gao*,Jihua Dong, Xiaofei Yang. Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks. PLoS ONE,2014, 9(3): e91856 【SCI IF=3.73,JCR=1】
[4] Songwei, Lin Gao*et al. Anti-triangle centrality-based community detection in complex networks. IET Systems Biology, 2014, 8(3), 116-125【 SCI IF= 1.672  JCR=2】.
[5] 王玙, 高琳. 基于社交圈的在线社交网络朋友推荐算法. 计算机学报. 2014,37(4):801~808
[6] 张恩利,高琳. 一种基于点割的电路划分算法. 计算机学报.2014,37(7):1528~1537
[7] Enli Zhang, Lin Gao*,A Vertex Separator-based Algorithm for Hypergraph Bipartitioning. Journal of Computers, 2014, 9(8): 1886-1896.
[8] Hao Wu,Lin Gao*,Feng Li, Fei Song, Xiaofei Yang. Network-Based Method for Identifying Overlapping Mutated Driver Pathways in Cancer  Bioinformatics Research and Applications: 10th International Symposium, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014: Proceedings, vol. 8492, p. 379. Springer, 2014.
[9] Feng Li,Lin Gao*. Detection of Core Cancer Modules by Mutated Gene Network in Glioblastoma IEEE International Conference on System Biology (ISB2014)， Qingdao, China, Oct. 24-27-20, 2014

[10] Xiaoke Ma, Long Gao, Kai Tan. Modeling disease progression using dynamics of pathway connectivity. Bioinformatics,2014,30(16) 2343~2350

2013
[1] Xingli Guo,Lin Gao*, et al. Long non-coding RNAs function annotation: a global prediction method based on bi-colored network.  Nucleic Acids Research,2013, 41(2): e35-e35
[2] Sun, Peng Gang, Lin Gao, and Yang Yang. Maximizing modularity intensity for community partition and evolution. Information Sciences,2013,236:83~92    
[3] Bingbo Wang, Lin Gao*,Yong Gao,Yue Deng.Maintain the structural controllability under malicious attacks on directed networks. Europhysics Letters,2013,101(5):58003 
[4] Yue Deng, Lin Gao*,Bingbo Wang. ppiPre: Predicting protein–protein interactions by combining heterogeneous features. BMC System Biology,2013,7(Suppl 2):S8 
[5] Yuxuan Hu, Lin Gao*, Kai Shi, David K.Y. Chiu. Detection of Deregulated Modules Using Deregulatory Linked Path. PLoS ONE,2013, 8(7): e70412. 
[6] Guimin Qin,Lin Gao*, Jianye Yang, Zhanying Xiong. Significant Substructure Discovery in Dynamic Networks. Current Bioinformatics,2013,8(1):46~55. 
[7] 郭杏莉,高琳*,刘永轩,党合萱等.长非编码RNA特征研究与分析.科学通报, 2013,58(27):2779~2786
[8] 高琳,杨建业,覃桂敏. 动态网络模式挖掘方法及应用.软件学报,2013,24(9):2042~2061
[9] Xingli Guo, Lin Gao*, Yongxuan Liu and Bingbo Wang. An MRF-based Method for lncRNA Function Prediction(Poster)  RECOMB2013
 
2012
[1] Xiaoke Ma, Lin Gao*, Biological network analysis: insights into structure and function. Briefing in Functional Genomics,2012,11(6):434~442 【IF=4.126,JCR=1】
[2] Xiaoke Ma, Lin Gao*, Discovering protein complexes in protein interaction networks via exploring the weak ties. BMC System Biology, 2012, 6(Suppl 1):S6【SCI IF=3.148,JCR=2】
[3] Xiaoke Ma,Lin Gao*. Predicting protein complexes in PPI networks by using acore-attachment algorithm based on graph communicability. Information Science ,2012,189:233~254  【SCI IF=2.833, JCR=1】
[4] Bingbo Wang, Lin Gao*,Yong Gao. Control range: a controllability-based index for node significance in directed networks. Journal of Statistical Mechanics: Theory and Experiment,2012,04:P04011【SCI IF=2.758, JCR=1】
[5] Bingbo Wang, Lin Gao*, Seed selection strategy in global network alignment without destroying the entire structures of functional modules. Proteome Science,2012,10(Suppl 1):S16 【SCI IF=2.49】
[6] Guimin Qin, Lin Gao*.An algorithm for network motif discovery in biological networks.  International Journal of Data Mining and Bioinformatics,2012,6(1):1~16. Press release
[7] 付立东,高琳,马小科. 基于社团检测的复杂网络中心性方法.中国科学F辑,2012,42(5):550~560
[8] Zengfa Dou, Lin Gao. A Particle Swarm Optimizer based Extension Classifier for Biomedical Document.Journal  of Computational Information Systems, 2012,8(1): 249~258.
[9] Zengfa Dou. Analyzing requirements of customer for WLAN using novel two-stage method. International Journal of Digital Content Technology and its Applications,2012,6(3): 93 -100.
[10] Yue Deng, Lin Gao. ppiPre - an R package for predicting protein-protein interactions. IEEE International Conference on System Biology (ISB2012). Proceedings, pp, 333~337. Xian, China, August 18-20, 2012

2011
[1] Peng Gang Sun,Lin Gao*. Identification of overlapping and non-overlapping community structure by fuzzy clustering. Information Science,2011, 181(6):1060-1071【SCI IF=3.291, JCR=1】
[2] Liang Yu, Lin Gao*,Chuiliang Kong. Identification of core-attachment complexes based on mining maximal frequent patterns in protein-protein interaction networks. Proteomics,2011,11(19):3826~3834【SCI IF=4.815,JCR=1】
[3] Xingli Guo, Lin Gao*. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations. PLoS One,2011,6(9): e24171【SCI IF=4.411, JCR=1】
[4] Xiaoke Ma, Lin Gao*.Non-traditional spectral clustering algorithms for community detection in complex networks: a comparative analysis. Journal of Statistical Mechanics: Theory and Experiment, 2011,05 : P05012【SCI IF=2.758, JCR=1】
[5] PengGang Sun,Lin Gao*. Prediction of human disease-related gene clusters by clustering analysis. International Journal of Biological Sciences, 2011,7(1), 61-73.【SCI IF=2.865 ,JCR=3】
[6] Liang Yu, Lin Gao*, Kui Li. A degree-distribution based hierarchical agglomerative clustering method for protein complexes identification. Computational Biology and Chemistry. 2011,35(5): 298-307【SCI IF=1.37】
[7] Peng gang Sun, Lin Gao.Identification of Conserved Protein Complexes by Module Alignment International Journal of Data Mining and Bioinformatics ,2011,6(5):593~610
[8] 鱼亮，高琳，孙鹏岗. 蛋白质网络中功能模块预测算法研究.计算机学报,2011, 34(7): 1239-1251
[9] Jian JI, Lin Gao. Speckle Reduction of Polarimetric SAR Images Based on Subband ICA Model. Journal of Computational Information Systems, 2011,7 (2) : 570-577.
[10] Bingbo Wang, Lin Gao. Global network alignment based on multiple hub seeds. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2011) ,pp, 29~34  Nov. 12-15, 2011
[11] Xiaoke Ma, Lin Gao. Detecting protein complexes in PPI networks: the roles of interactions. IEEE International Conference on System Biology (ISB 2011). Proceedings, pp, 52~59. Zhuhai, China, September 2-4.2011
[12] Yu Wang, Lin Gao and Zhe Chen. An edge based core-attachment method to detect protein complexes in PPI networks. IEEE International Conference on System Biology (ISB2011). Proceedings, pp, 72~77. Zhuhai, China, September 2-4.2011
[13] Guimin Qin, Lin Gao. IEEE International Conference on Signal Processing (ICSP11)
[14] Li dong Fu, Lin Gao. An eigenvector-based kernel clustering approach to detecting communities in complex networks. IEEE International Conference on Computer Science and Automation Engineering (CSAE2011), Shanghai, China, June 10-12,2011

2010                                        
[1] Xiaoke Ma, Lin Gao*, Xuerong Yong. Eigenspaces of networks reveal the overlapping and hierarchical community structure more precisely Journal of Statistical Mechanics: Theory and Experiment 2010,08 : P08012 【SCI IF=2.758】  【SCI:000281744800019】 
[2] Xiaoke Ma, Lin Gao*,Xuerong Yong, Lidong Fu. Semi-supervised clustering for community structuredetection in complex networks  Physica A, 2010,389(1):187~197.   【SCI IF=1.562】
[3] Guimin Qin, Lin Gao*.Spectral Clustering for Detecting Protein Complexes in PPI Networks.  Mathematical and Computer Modelling, 2010,52 (11-12) : 2066~2074  【SCI IF=1.103】
[4] Liang Yu, Lin Gao*, Peng gang Sun. A Hybrid Clustering Algorithm for Identifying Modularity in Protein-Protein Interaction Networks. International Journal of Data Mining and Bioinformatics 2010, 5(4):600~615   SCI IF=0.933
[5] Li dong Fu, Lin Gao.  A centrality measure based on spectral optimization of modularity density Science in China Series F, 2010,53(9):1727~1737
[6] 郭杏莉, 高琳, 陈新. 生物网络比对的模型与算法. 软件学报, 2010,21(9): 2089~2106
[7] 赵建邦, 董安国, 高琳. 一种用于生物网络数据的频繁模式挖掘算法. 电子学报, 2010, 38(8): 1803 ~ 1807    
[8] Liang Yu, Lin Gao. Quantitative Function for Community Structure Detection. International Journal of Data Mining, Modelling and Management,2010, 4(2):351~368
[9] Liang Yu, Lin Gao, A method based on local density and random walks for complexes detection in protein-protein interaction networks. Journal of Bioinformatics and computational Biology. 2010，8(Suppl. 1):1~16 
[10] Liang Yu, Lin Gao. An average-degree based method for protein complexes identification. The Fourth International Conference on Bioinformatics and Biomedical Engineering  (iCBBE2010) Proceedings, pp. 175-182 , June 18-20, Chengdu, China. Jian Bang Zhao, Lin Gao. An algorithm for extracting subgraph of specific species from me[ant]tabolic pathway. The Fourth International Conference on Bio-Inspired Computing: Theories and Applications, (BIC-TA2010) Proceedings, pp, 74~79. Sep 23 -26, 2010 Changsha, China.
[11] Yu Wang, Lin Gao. An improved AP algorithm for identifying overlapping functional modules in protein-protein interaction networks. IEEE International Conference on. Signal Processing (ICSP10)  Proceedings, pp, 1809~1812. October 24 -28, 2010 Beijing, China.
[12] Liang Yu, Lin Gao. Identification of core-attachment complexes based on mining maximal frequent patterns in protein-protein interaction networks. IEEE International Conference on Bioinformatics and Biomedicine  (IEEE BIBM 2010)  pp, 29~34. Dec. 18-21, 2010, Hong Kong 
 
2009  以前
[1] Lin Gao，Peng gang Sun, Jia Song. Clustering Algorithms for detecting functional modules in protein interaction networks. Journal of Bioinformatics and Computational Biology, 2009, 7(1):217-242.
[2] 赵建邦,高琳, 宋佳. 基于代谢路径构建系统发生树的方法.   电子学报, 2009,37(8):1633~1638.
[3] 覃桂敏, 高琳. 生物网络模体发现算法研究综述. 电子学报, 2009, 37(10):2258~2265.
[4] 覃桂敏, 高琳, 周晓锋. 非树型网络模体发现算法. 电子学报,2009, 37(11):2420~2426.
[5] 高琳，覃桂敏，周晓峰.  图数据中频繁模式挖掘算法研究综述. 电子学报, 2008,36（8）:1603~1609.
[6] Jialu Hu, Lin Gao, Gui min Qin.  Evaluation of subgraph searching algorithms detecting network motif in biological networks.  Frontiers of Computer Science in China, 2009,3(3):412~416
[7] Peng gang Sun, Lin Gao. A fast iterative-clique percolation method for identifying functional modules in protein interaction networks. Frontiers of Computer Science in China, 2009,3(3):405~411
[8] 董安国，高琳等. 基于环分布的频繁子图挖掘算法.工程数学学报, 2009,26(6):977~984
[9] Peng gang Sun , Lin Gao. Algorithms based on Density and Shared Neighbors for Functional Modules Identification in PPI Networks. IEEE International Conference on Bioinformatics and Bioengineering BIBE2009 – Proceedings, pp.228-235, June 22-24, 2009 in Taichung, Taiwan   
[10] Peng gang Sun, Lin Gao.  Fast algorithms for detecting overlapping functional modules in PPI networks. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings, pp. 247-254, 2009  March 30-April 2, 2009 in Nashville, U.S.A     
[11] Guimin Qin, Lin Gao. Spectral Clustering for Detecting Protein Complexes in PPI Networks The Fourth International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA2009- Proceedings, pp. 175-182 , October 16 -19, 2009 in Beijing, China 
[12] Lin Gao，YanZeng , Anguo Dong.  An ant colony algorithm for solving Max-cut problems.
    Progress in Natural Science, 2008, 18 (9):1173~1178
[13] Xiahong Lin, Lin Gao, Kefei Chen . A Clustering Algorithm for Mining Overlapping Highly Connected Subgraphs.  The 2nd International Conference on Bioinformatics and Biomedical Engineering (iCBBE2008)  May 16th to 18th, 2008 in Shanghai, China.pp.523~526
[14] Wenbin Liu,Xiangou Zhu, Lin Gao. The Hamiltonian Cycle Problem Based on DNA Computing. International Journal of Unconventional Computing. 2007,3(2),69-77.
[15] Xiaofeng Zhou, Lin Gao，AnGuo Dong.  An Algorithm for Finding Frequent Patterns in a Large Sparse Graph  IAENG International Conference on Bioinformatics (ICB2007) Hong Kong, 12-23 March, 2007  pp.290-294 
[16] An guo Dong, Lin Gao, Xiaofeng Zhou，HongYu Su.  An algebra approach for finding frequent subgraphs with Hamilton cycle   The 4th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD07)  Haikou, 24-27,August, 2007 pp.288~292 
[17] Lin Gao，YanZeng , Haiping Zhan. An Ant Colony Algorithm for Solving Maximum Cut Problems. The second International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA2007) Zhengzhou, 14-17, September,2007 pp.158~161
[18] Lin Gao, Lin Wenbin, Weifeng Li，David K.Y. Chiu. Self-Assembly Based Models and Algorithms for DNA Computation  International Journal of Unconventional Computing  2006,2: 267~280
[19] Wenbin Liu, Lin Gao, Qiang Zhang, Guandong Xu, Xiangou Zhu, Xiangrong Liu and Jin Xu. A Random Walk DNA Algorithm for the 3-SAT Problem. Current Nanoscience, 2005, 1, 85-90  

[20]  Lin Gao, David K.Y. Chiu.  Classification algorithms and analyzing functionality of protein families. Sixth International Conference on Data Mining, Text Mining and their Business Applications , 25 - 27 May 2005 , Skiathos, Greece.  pp. 431-443 
[21] Liu WenBin, Zhangqiang, Gao Lin. A DNA based evolutionary algorithm for the minimal set cover problem.  2005 International Conference on Intelligent Computing (ICIC2005)  Part Ⅱ LNCS3645, pp.80-89,2005 
[22] Gao Lin, Yang Xiao, Liu Wenbin, Jin Xu.  DNA Based Model for Addition Computation.  Progress in Natural Science  2004,14(8):705~709
[23] 高琳，许进.基于分子计算的最小顶点覆盖问题的DNA算法 系统工程与电子技术 2004,26(4)：544~548    
[24] 马润年，张强，高琳，许进. 图的最大权团的DNA计算 电子学报2004,32(1):13~16
[25] 高琳，许进. 图的顶点着色问题的DNA算法.   电子学报.  2003,31(4):14~18
[26] Wenbin Liu, Lin Gao, Shudong Wang, Jin Jin Xu. Solving the 3-SAT Problem Based on DNA Computing.  Journal of Chemical Information and Computer Sciences. 2003, 43, 1872~1875  
[27] 张强, 高琳, 王超, 袁涛，许进. 具有时滞的一阶细胞神经网络的动态行为研究. 物理学报, 2003,52(7):1606~1610 
[28] 刘文斌，高琳，王淑栋，许进。 最大匹配问题的DNA表面计算模型. 电子学报. 2003，31(10):1496~1499
[29] Wenbin Liu,Shudong Wang, Lin Gao, Jin Xu. DNA Sequence Design based on Template Strategy . Journal of Chemical Information and Computer Sciences.  2003, 43, 2014-2018 
[30] 张强, 高琳, 王超, 许进. 时滞双向联想记忆神经网络的全局稳定性. 物理学报， 2003, 52(7):1060~1065 
[31] 高琳，马润年，许进. 基于质粒求解最大匹配问题的DNA算法. 生物化学与生物物理进展.  2002,29(5): 820~823  
[32] Gaolin, Ma Runnian ,Xujin.   DNA Solution of Vertex Cover Problem Based on Sticker Model.   Chinese Journal of Electronics. 2002,11(2):280~284.
[33] 高琳，马润年，许进.  有向最短哈密尔顿路问题的DNA算法.  系统工程与电子技术, 2002,24(8):102~105
[34] Gaolin , Xujin.  The Typical Models and Algorithm for DNA Computing. The Sixth Biennial  International Conference for young Computer Scientists (ICYCS 2001) , 2001,10，Zhejiang,China, pp489-493.
[35] Gaolin，Xujin. Reconfiguration Algorithm for Degradable VLSI/WSI Arrays Based  on Neural Network. The 8th International Conference on Neural Information Processing(ICONIP2001)2001,11, Shanghai,Chian, pp128-131.
[36] 高琳，许进，张军英. DNA计算的研究进展与展望.  电子学报, 2001,29 (7):945-949.
[37] 高琳，张军英，许进.  基于神经网络的单通道冗余VLSI/WSI阵列重构算法. 电子学报,  2001,29 (12):1683-1686 
[38] 高琳，许进，张军英.  平面测试问题的一种新型的神经网络算法. 西安电子科技大学学报, 2001,28(2):245-248
[39] 高琳，张军英，许进.  基于神经网络的降阶VLSI/WSI阵列重构算法. 西安电子科技大学学报, 2001,28(3):287-291

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