教育经历：

（1）1985年9月至1989年7月在中国地质大学计算机科学系计算机应用专业学习，获学士学位。

（2）1994年9月至1997年6月在国防科技大学计算机系计算机应用专业学习，获工学硕士学位。

（3）2003年3月至2007年12月在国防科技大学计算机学院学习，师从陈火旺院士和王戟教授，获博士学位。

工作经历：

（1）     1989年8月至2000年10月在位于新疆马兰的中国核武器试验基地工作，从事核试验安全保障技术研究与核武器试验数据计算机处理工作。

（2）     2000年10月至今在湖南大学信息科学与工程学院从事科研与教学工作。

（3）     2008年4月至2010年8月在中国科学院合肥物质科学研究院博士后流动站工作。

（4）     2012年5月至2013年5月获美国资助, 在美国Kansas大学应用生物信息学实验室做博士后研究。

主持科研项目：

(1)国家重点研发计划课题： 基于超级计算的肿瘤大数据分析技术与人工智能诊断标准研究（2017YFC1311003），主持，起止时间： 2018.1.1至2021.06.30。

(2)国家自然科学基金: 体细胞突变影响癌症进程的随机动力学模型研究（61672011）, 主持，起止时间：2017.1.1至2020.12.31。

(3)国家自然科学基金: 基于新一代肿瘤测序数据的驱动通路发现与综合分析方法研究(61472467), 主持，起止时间：2015.1.1至2018.12.31。

(4)国家自然科学基金：基于启发式信息的肿瘤基因表达谱降维与分析方法研究(60973153),主持，起止时间：2010.01.01至2012.12.31。

(5)中国博士后科学基金 (20090450825)，主持,起止时间：2009.01.01至2010.12.31。

（1）      WeiZhang, Shu-Lin Wang*, Inference ofCancer Progression With Probabilistic Graphical Model From Cross-SectionalMutation Data, IEEE ACCESS, vol.6, Pages: 22889-22898, 2018.

（2）      WeiZhang, Shu-Lin Wang,  An IntegratedFramework for Identifying Mutated Driver Pathway and Cancer Progression,IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, vol.16, no.2,Pages:455-464, 2019.

（3）      WeiZhang, Shu-Lin Wang*, An efficientstrategy for identifying cancer-related key genes based on graph entropy,COMPUTATIONAL BIOLOGY AND CHEMISTRY, vol.74, Pages:142-148, 2018.

（4）      ChaoPei, Shu-Lin Wang*, Jianwen Fang, GSMC:Combining Parallel Gibbs Sampling with Maximal Cliques for Hunting DNA Motif,JOURNAL OF COMPUTATIONAL BIOLOGY, vol.24, no.12, Pages:1243-1253, 2017.

（5）      DanLuo, Shu-Lin Wang*, Jianwen Fang, MIMPFC:Identifying miRNA-mRNA regulatory modules by combining phase-only correlationand improved rough-fuzzy clustering, JOURNAL OF BIOINFORMATICS ANDCOMPUTATIONAL BIOLOGY, vol.16, no.1,Article ID: 1750028, 2018.

（6）      WenLi, Shulin Wang*, Junlin Xu, Guo Mao, Geng Tian, Jialiang Yang，InferringLatent Disease-lncRNA Associations by Faster Matrix Completion on aHeterogeneous Network, FRONTIERS IN GENETICS, vol.10, Article ID:769,2019.

（7）      YueLiu, Shu-Lin Wang*, Jun-Feng Zhang, Prediction ofMicrobe-Disease Associations by Graph Regularized Non-Negative MatrixFactorization, JOURNAL OF COMPUTATIONAL BIOLOGY, vol.25, no.12,Pages: 1385-1394, 2018.

（8）      GuoMao, Shu-Lin Wang*, Wei Zhang, Prediction ofPotential Associations Between MicroRNA and Disease Based on BayesianProbabilistic Matrix Factorization Model, JOURNAL OF COMPUTATIONALBIOLOGY, vol.26, no.9, Pages:1030-1039, 2019.

（9）      Wei Zhang, Shu-Lin Wang*, A NovelMethod for Identifying the Potential Cancer Driver Genes Based on MolecularData Integration, BIOCHEMICAL GENETICS, vol.58, no.1, Pages:16-39,2020.

（10）   Su Zhou, Shulin Wang*, Qi Wu, Riasat Azim , Wen Li，Predictingpotential miRNA-disease associations by combining gradient boosting decisiontree with logistic regression，COMPUTATIONAL BIOLOGY AND CHEMISTRY，vol. 85,Article ID: 107200, 2020.

（11）   YueLiu, Shu-Lin Wang*, Jun-Feng Zhang, Wei Zhang, and Wen Li, LncRNA-diseaseassociations prediction based on neural network-based matrix factorization,IEEE Access, Digital Object Identifier 10.1109/ACCESS.2017.Doi Number, 2020.

（12）   LeiTian and Shu-Lin Wang*, Exploring MiRNA Sponge Networks of breast cancerby Combining miRNA-disease-lncRNA and miRNA-target Networks, CurrentBioinformatics, vol.16, no.3, Pages: 385-394, 2021.

（13）   LeiTian and Shu-Lin Wang*, Exploring the potential microRNA spongeinteractions of breast cancer based on some known interactions, Journal ofBioinformatics and Computational Biology, vol.18, no.3, Article ID: 2050007 (15pages), 2020.

（14）   RiasatAzim , Shulin Wang*, Su Zhou, Xing Zhong, Purity estimation fromdifferentially methylated sites using Illumina Infinium methylation microarraydata, CELL CYCLE, vol.19, no.16, Pages:2028-2039, 2020.

（15）   YueLiu, Shu-Lin Wang*, Jun-Feng Zhang, Wei Zhang, Su Zhou and Wen Li,DMFMDA: Prediction of microbe-disease associations based on deep matrixfactorization using Bayesian Personalized Ranking, IEEE-ACM TRANSACTIONS ONCOMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2021.

（16）   YueLiu, Shu-Lin Wang*, Jun-Feng Zhang, Wei Zhang and Wen Li. A neuralcollaborative filtering method for identifying miRNA-disease associations, Neurocomputing,vol.422, Pages: 176-185, 2021.

（17）   Wen Li,Shu-Lin Wang*, Junlin Xu, and Jialiang Yang，Identificationof Human LncRNA-Disease Association by Fast Kernel Learning-Based KroneckerRegularized Least Squares，ICIC 2020: Intelligent Computing Theories andApplication，vol.12464，Pages: 302-315，Bari, Italy,2020.

（18）   Cai,Jie, Huang, Wei, Yang, Sheng, Wang, Shulin, Luo, Jiawei, A SelectionMethod for Denoising Auto Encoder Features Using Cross Entropy, Lecture Notesin Computer Science (including subseries Lecture Notes in ArtificialIntelligence and Lecture Notes in Bioinformatics), 15th InternationalConference on Intelligent Computing, ICIC 2019, vol. 11645, Pages: 479-490, Nanchang,China, 2019.

（19）   Mao,Guo, Wang, Shu-Lin*, A Novel Approach to Predicting MiRNA-DiseaseAssociations, Intelligent Computing Theories and Application, InternationalConference on Intelligent Computing, vol.11644, Pages: 354-365, Nanchang,China, 2019.

（20）   Cai,Jie, Luo, Jiawei, Shulin Wang, Yang, Sheng*, Feature selection inmachine learning: A new perspective，NEUROCOMPUTING，vol.300, Pages: 70-79, 2018.

（21）   Shu-Lin Wang, Yaping Fang, Jianwen Fang. Diagnostic prediction of complexdiseasesusing phase-only correlation based on virtual sample template.BMCBioinformatics, vol.14(Suppl 8):S11, 2013.

（22）   Shu-LinWang, Yi-Hai Zhu, Wei Jia, Deshuang Huang. RobustClassification Method of Tumor Subtype by Using Correlation Filters. IEEE-ACMTransactions on Computational Biology and Bioinformatics, vol. 9, no.2, Pages:580-591, 2012.

（23）   Shu-LinWang, Xue-Ling Li, Jian-Wen Fang, (2012). Findingminimum gene subsets with heuristic breadth-first search algorithm for robusttumor classification. BMC Bioinformatics, vol.13, Article ID:178, 2012.

（24）    Shu-LinWang, Jie Gui, and Xue-Ling Li, Factor analysis for cross-platform tumorclassification based on gene expression profiles,  Journal of Circuits, Systems, and Computers,vol.19, no.1, Pages: 243-258, 2010.

（25）    Shu-LinWang, Xueling Li,Shanwen Zhang, Jie Gui, and Deshuang Huang, Tumorclassification by combining PNN classifier ensemble with neighborhood rough setbased gene reduction, Computers in Biology and Medicine, vol.40, no.2, Pages: 179-189,2010.

（26）   Shu-LinWang, XueLing Li, Jun-Feng Xia, and Xiao-PingZhang, Weighted neighborhood classifier for the classification of imbalancedtumor dataset, Journal of Circuits, Systems, and Computers, vol.19, no.1, Pages:259-273, 2010.

（27）   王树林, 王戟,陈火旺, 李树涛,张波云, “肿瘤信息基因启发式宽度优先搜索算法研究,”《计算机学报》,vol.31, no.4, Pages: 636-649, 2008.

（28）   王树林, 王戟,陈火旺, 张鼎兴,“k-长DNA子序列计数算法研究,”《计算机工程》, vol.33, no.9, Pages:40-42, 2007.

（29）   王树林, 王戟,陈火旺, 张波云,“基于主成份分析的肿瘤分类检测算法研究,” 《计算机工程与科学》,vol.29, no.9, Pages: 84-89, 2007.

（30）   王树林, 王戟,陈火旺, 张波云,“基于分形的DNA序列可视化表示研究,”《计算机科学》,vol.33, no.7, Pages: 158-163, 2006.

（31）   王树林, 王戟,陈火旺, 张鼎兴,“k-长DNA子序列频数分布研究,”《生物物理学报》,vol.22, no.3, Pages: 177-196, 2006.

（32）   MeilingHou, Shu-Lin Wang*, Xueling Li, Ying-ke Lei. Neighborhood rough setreduction based gene selection and prioritization for gene expression profileanalysis and molecular cancer classification, Journal of Biomedicine andBiotechnology, Article ID: 726413, 12 pages, 2010.

（33）   JieGui,  Shu-Lin Wang, and Ying-ke Lei, Multi-step DimensionalityReduction and Semi-Supervised Graph-Based Tumor Classification Using GeneExpression Data," Artificial Intelligence in Medicine, vol.50, no.3,Pages: 181-191, 2010.

（34）   JieGui, Wei Jia, Ling Zhu, Shulin Wang and Deshuang Huang, LocalityPreserving Discriminant Projections for Face and Palmprint Recognition,Neurocomputing, vol.73, no.13, Pages: 2696-2707, 2010.