四川大学化学学院
师资力量

李梦龙     教 授

研究方向:分析化学

联系方式:          Email:liml@scu.edu.cn

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简历

1978-1990年 湖南大学分析化学专业分获理学学士、硕士、博士学位(1985-1987年在湖南大学任教)

1990-至今四川大学化学学院任教,现为四川大学教授,博士生指导教师。

主要研究方向

研究方向和课题组介绍:

生物信息学:蛋白质分类与相互作用、药物-蛋白质(基因)-疾病关联算法研究,组学数据解析与调控网络建模。  

化学信息学:机器学习在化学、生物医学数据中的应用,计算机辅助药物设计,化学计量学算法及应用。  

本课题组在数据挖掘方面积累了丰富的经验,近几年来我们开发了数个适用于不同分析任务的工具,如适用于多种机器学习方法并行又省时的工具,用于蛋白质相互作用预测的web-sever,关于药物靶标相互作用关系预测算法的改进等,并构建了相应的模型与预测系统进行聚类分析,分类分析和关联分析等任务。我们与军工单位合作,基于化学计量学方法研发出了炸药数据库及智能配方系统,受到好评。  

我们采用机器学习和信号处理等方法对蛋白质结构和功能进行了深入研究。如蛋白质结构域、甲基化位点、磷酸化位点、蛋白 - 蛋白的结合亲和力和单氨基酸多态性与疾病的关联等。另一方面,我们将化学计量学方法应用于分子动力学模拟和光谱解析。此外研究还涉及到药物不良反应的预测,疾病相关基因的识别,microRNA前体的识别,microRNAmRNA相互作用。针对各种生物问题,我们在特征的选取和利用以及模型的替代上积累了较丰富的经验。采用蛋白质的序列信息实现了对蛋白质域和蛋白质甲基化位点的预测;利用相互作用信息(蛋白-蛋白相互作用面特征,域-域相互作用,药物-靶标相互作用)构建的预测模型均取得了较好的结果(典型蛋白质结构功能信息的数字化表征, 2014年度教育部高等学校自然科学2等奖)。

在算法方面,本课题组不断尝试对各类算法的应用和改进。提出了基于特征值转换的数学方法用于药物靶标的预测;用于药物不良反应预测的新的统计算法;基于模拟退火优化的网络社区划分方法用于人类细胞信号网络的研究和分析。

主要工作业绩

教学方面:担任教育部高校理科化学指导委员会委员、中国化学会化学教育委员会成员、计算机化学专业委员会委员,参与了由教指委承担的《本科化学专业规范》和《本科化学教学质量评估》等项目,负责教育部、四川省教改项目4项。主编计算机辅助化学教材《Internet与化学信息导论》、《化学软件及其应用》、《分析化学数据速查手册》、《元素化学反应速查手册》,及国家11.5规划教材《化学信息学》;负责四川省精品课程《分析化学》的建设,获省级教学成果奖2项,并获得四川大学教学名师称号。

科研方面:课题组通过多年研究和不断创新,在化学计量学,化学和生物信息相关的算法和体系研究上取得了较好的成果。如生物分子的结构功能研究,生物组学分析领域积累了丰富的经验;在临床数据的诊断模型构建以及软件平台建设方面积累了丰富的经验。负责了多项国家自然科学基金项目(目前在研2项),在Nucleic Acids Research, SCI Rep, PROTEINS, 以及Chemometrics and Intelligent Laboratory SystemsPLoS系列,BMC 系列, 杂志发表SCI论文200余篇。

代表性成果 (获奖成果、专著、论文、专利)

2017 年:

1. Xue J, Xie F, Xu J, et al. A New Network-Based Strategy   for Predicting the Potential miRNA-mRNA Interactions in Tumorigenesis:[J].   International Journal of Genomics, 2017, 2017(1):3538568.

2. Yang Y, Xie F, Yan B, et al. A reliable multiclass   classification model for identifying the subtypes of parotid neoplasms   constructed with variable combination population analysis and partial least   squares regression based on Raman spectra[J]. Chemometrics & Intelligent   Laboratory Systems, 2017.

3. Xie F, He M, Li H, et al. Bipartite network analysis   reveals metabolic gene expression profiles that are highly associated with   the clinical outcomes of acute myeloid leukemia[J]. Computational Biology   & Chemistry, 2017, 67(C):150.

4. Liu Y, Liang Y, Kuang Q, et al. Post‐modified non‐negative   matrix factorization for deconvoluting the gene expression profiles of   specific cell types from heterogeneous clinical samples based on   RNA‐sequencing data[J]. Journal of Chemometrics, 2017.

5. Kuang Q, Li Y, Wu Y, et al. A kernel matrix dimension   reduction method for predicting drug-target interaction[J]. Chemometrics   & Intelligent Laboratory Systems, 2017, 162:104-110.

6. Li Y, Dong Y, Huang Z, et al. Computational identifying   and characterizing circular RNAs and their associated genes in hepatocellular   carcinoma.[J]. PLoS One, 2017, 12(3):e0174436.

7. Dong Y, Huang Z, Kuang Q, et al. Expression dynamics and   relations with nearby genes of rat transpoSsable elements across 11 organs, 4   developmental stages and both sexes[J]. BMC Genomics, 2017, 18(1):666.

8. Wu Y, Jing R, Dong Y, et al. Functional annotation of   sixty-five type-2 diabetes risk SNPs and its application in risk   prediction:[J]. Scientific Reports, 2017, 7.

9. Wang Y, Guo Y, Pu X, et al. A sequence-based   computational method for prediction of MoRFs[J]. RSC Advances, 2017,   7(31):18937-18945.

10. Wang Y, Lin Y, Guo Y, et al. Functional dissection of   human targets for KSHV-encoded miRNAs using network analysis:[J]. Scientific   Reports, 2017, 7(1):3159.

11. Li W, Li M, Pu X, et al. Distinguishing the disease   associated SNPs based on composition frequency analysis[J]. Interdiscip Sci :   Comput life Sci, 2017, 9:459–467.

12. Wang Y, Guo Y, Pu X, et al. Effective prediction of   bacterial type IV secreted effectors by combined features of both C-termini   and N-termini[J]. J Comput Mol Des, 2017, 31:1029–103.

2016 年:

1. Wen Z, Chen G, Zhu S, Zhu J, Li B, Song Y, Li S, Shi L,   Zheng Y, Li M. Expression profiling and functional annotation of   noncoding genes across 11 distinct organs in rat development. Scientific   Reports. 2016638575.

2. Gao N, Liang T, Yuan Y, et al. Exploring the mechanism   of F282L mutation-caused constitutive activity of GPCR by a computational   study.[J]. Physical chemistry chemical physics : PCCP, 2016, 18(42):29412.

3. Lu T, Yuan Y, Jiao Y, et al. Simultaneous   spectrophotometric quantification of dinitrobenzene isomers in water samples   using multivariate calibration methods[J]. Chemometrics & Intelligent   Laboratory Systems, 2016, 154:72-79.

4. Jiao Y, Li M, Wang N, et al. A facile color-tuning   strategy for constructing a library of Ir(III) complexes with fine-tuned   phosphorescence from bluish green to red using a synergetic substituent   effect of –OCH3 and –CN at only the C-ring of C^N ligand[J]. Journal of   Materials Chemistry C, 2016, 4(19):4269-4277.

5. Liu Z, Guo Y, Pu X, et al. Dissecting the regulation   rules of cancer-related miRNAs based on network analysis[J]. Scientific   Reports, 2016, 6.

6. Jiang Y, Yuan Y, Zhang X, et al. Use of network model to   explore dynamic and allosteric properties of three GPCR homodimers[J]. RSC   Advances, 2016, 6(108).

7. Wang M, He X, Xiong Q, et al. A facile strategy applied   to simultaneous qualitative-detection on multiple components of mixture   samples: A joint study of infrared spectroscopy and multi-label algorithms on   PBX explosives[J]. RSC Advances, 2016, 6(6):4713-4722.

8. Yi J, Xiong Y, Cheng K, et al. A Combination of   Chemometrics and Quantum Mechanics Methods Applied to Analysis of Femtosecond   Transient Absorption Spectrum of Ortho-Nitroaniline[J]. Scientific Reports,   2016, 6:19364.

9. Zhang L, Li Y, Yuan Y, et al. Molecular mechanism of   carbon nanotube to activate Subtilisin Carlsberg in polar and non-polar   organic media:[J]. Sci Rep, 2016, 6:36838.

10. Zeng X, Zhang L, Xiao X, et al. Unfolding mechanism of   thrombin-binding aptamer revealed by molecular dynamics simulation and Markov   State Model[J]. Scientific Reports, 2016, 6:24065.

11. Wu Y, Kuang Q, Dong Y, et al. Predicting pathogenic   single nucleotide variants through a comprehensive analysis on multiple level   features[J]. Chemometrics & Intelligent Laboratory Systems, 2016,   156:224-230.

12. Xu J, Jing R, Liu Y, et al. A new strategy for   exploring the hierarchical structure of cancers by adaptively partitioning   functional modules from gene expression network:[J]. Scientific Reports,   2016, 6:28720.

2015 年:

1. Dai X, Jing R Y, Guo Y, et al. Predicting the   druggability of protein-protein interactions based on sequence and structure   features of active pockets.[J]. Current Pharmaceutical Design, 2015,   21(21):3051-61.

2. Dong Y, Kuang Q, Dai X, et al. Improving the Understanding   of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein   Interaction Network.[J]. BioMed Research International, 2015, 2015:   890381.

3. Fu Y, Guo Y, Wang Y, et al. Exploring the relationship   between hub proteins and drug targets based on GO and intrinsic disorder[J].   Computational Biology & Chemistry, 2015, 56(C):41-48.

4. Hu Y, Guo Y, Shi Y, et al. A consensus subunit-specific   model for annotation of substrate specificity for ABC transporters[J]. RSC   Advances, 2015, 5(52):42009-42019.

5. Huang L, Jing R, Yang Y, et al. Characteristic   wavenumbers of Raman spectra reveal the molecular mechanisms of oral   leukoplakia and can help to improve the performance of diagnostic models[J].   Analytical Methods, 2015, 7(2):590-597.

6. Jing R, Sun J, Wang Y, et al. Domain position prediction   based on sequence information by using fuzzy mean operator[J]. Proteins   Structure Function & Bioinformatics, 2015, 83(8):1462-1469.

7. Jing R, Li R, Pu X, et al. A Web-based Graphic User   Interface of PML for Machine Learning in Parallel Running. Chemical   informatics,2015,1:2-7.

8. Li R, Dong Y, Kuang Q, et al. Inductive matrix   completion for predicting adverse drug reactions (ADRs) integrating   drug–target interactions[J]. Chemometrics & Intelligent Laboratory   Systems, 2015, 144:71-79.

9. Liu Y, Jing R, Xu J, et al. Comparative analysis of   oncogenes identified by microarray and RNA-sequencing as biomarkers for   clinical prognosis[J]. Biomarkers in Medicine, 2015, 9(11):1067-78.

10. Lu T, Yuan Y, He X, et al. Simultaneous determination   of multiple components in explosives using ultraviolet spectrophotometry and   a partial least squares method[J]. RSC Advances, 2015, 5(17):13021-13027.

11. Luo J, Liu Z, Guo Y, et al. A structural dissection of   large protein-protein crystal packing contacts[J]. Scientific Reports, 2015,   5:14214.

12. Shi Y, Guo Y, Hu Y, et al. Position-specific prediction   of methylation sites from sequence conservation based on information   theory[J]. Scientific Reports, 2015, 5(6):12403.

13. Wang Y, Guo Y, Kuang Q, et al. A comparative study of   family-specific protein–ligand complex affinity prediction based on random   forest approach[J]. Journal of computer-aided molecular design, 2015,   29(4):349-60.

14. Kuang Q, Xu X, Li R, et al. An eigenvalue   transformation technique for predicting drug-target interaction[J].   Scientific Reports, 2015, 5:13867.

2014 年:

1. Luo J, Guo Y, Zhong Y, et al. A functional feature analysis   on diverse protein-protein interactions: application for the prediction of   binding affinity.[J]. Journal of computer-aided molecular design, 2014,   28(6):619-29.

2. Yang X, Guo Y, Luo J, et al. Effective identification of   Gram-negative bacterial type III secreted effectors using position-specific   residue conservation profiles[J]. PLOS One, 2013, 8(12):e84439.

3. Ma D, Guo Y, Luo J, et al. Prediction of protein–protein   binding affinity using diverse protein–protein interface features[J].   Chemometrics & Intelligent Laboratory Systems, 2014, 138:7-13.

4. Zhong Y, Guo Y, Luo J, et al. Effective identification   of kinase-specific phosphorylation sites based on domain–domain   interactions[J]. Chemometrics & Intelligent Laboratory Systems, 2014,   136(16):97-103.

5. Luo J, Guo Y, Fu Y, et al. Effective discrimination   between biologically relevant contacts and crystal packing contacts using new   determinants.[J]. Proteins-structure Function & Bioinformatics, 2014,   82(11):3090.

6. Jing R, Sun J, Wang Y, et al. PML: A parallel machine   learning toolbox for data classification and regression[J]. Chemometrics   & Intelligent Laboratory Systems, 2014, 138:1-6.

7. Wang Y, Jing R, Hua Y, et al. Classification of   multi-family enzymes by multi-label machine learning and sequence-based   descriptors[J]. Analytical Methods, 2014, 6(17):6832-6840.

8. He L, Wang Y, Yang Y, et al. Identifying the Gene   Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model   Performance on Predicting the Cancer Prognosis[J]. Biomed Research   International, 2014, 2014(4):424509.

9. Jiang L, Huang L, Kuang Q, et al. Improving the   prediction of chemotherapeutic sensitivity of tumors in breast cancer via   optimizing the selection of candidate genes.[J]. Computational Biology &   Chemistry, 2014, 49(1):71-78.

10. Wu D, Yang G, Zhang L, et al. Genome-wide association   study combined with biological context can reveal more disease-related SNPs   altering microRNA target seed sites[J]. BMC Genomics, 2014, 15(1):669.

11. Li Chen, Yongzhi Zhang, Chaohong Lin, Wen Yang, Yan   Meng, Yong Guo, Menglong Li,* Dan Xiao,* Hierarchically porous nitrogen-rich   carbon derived from wheat straw as an ultrahigh-rate anode for lithium ion   battery, Journal of Materials Chemistry A 2 (2014) 9684-9690

12. Hu J, Luo Q, Zhang Z, et al. Self-assembled nanopillar   arrays by simple spin coating from blending systems comprising PC61BM and   conjugated polymers with special structure[J]. RSC Advances, 2014,   4(46):24316-24319.

13. Wu Y, Jing R, Jiang L, et al. Combination use of   protein-protein interaction network topological features improves the   predictive scores of deleterious non-synonymous single-nucleotide   polymorphisms.[J]. Amino Acids, 2014, 46(8):2025-2035.

14. Zhu Y, Yuan Y, Xiao X, et al. Understanding the effects   on constitutive activation and drug binding of a D130N mutation in the β2   adrenergic receptor via molecular dynamics simulation[J]. Journal of   Molecular Modeling, 2014, 20(11):1-12.

15. Kuang Q, Wang M, Li R, et al. A systematic   investigation of computation models for predicting Adverse Drug Reactions   (ADRs).[J]. PLOS One, 2014, 9(9):e105889-e105889.

2013 年:

1. Liu W, Guo Y, Luo J, et al. Prediction of   kinase-specific phosphorylational interactions using random forest[J].   Chemometrics & Intelligent Laboratory Systems, 2013, 126(22):117-122.

2. Yu L, Luo J, Guo Y, et al. In silico identification of   Gram-negative bacterial secreted proteins from primary sequence.[J].   Computers in Biology & Medicine, 2013, 43(9):1177-1181.

3. Zhang L, Zhang J, Gang Y, et al. Investigating the   concordance of Gene Ontology terms reveals the intra- and inter-platform   reproducibility of enrichment analysis[J]. BMC Bioinformatics, 2013,   14(1):143.

4. Zhang J, Zhang L, Yang G, et al. Nonnegative matrix   factorization for the improvement in sensitivity of discovering potentially   disease-related genes[J]. Chemometrics & Intelligent Laboratory Systems,   2013, 126(126):100-107.

5. Sun J, Jing R, Wu D, et al. The effect of edge   definition of complex networks on protein structure identification[J]. Comput   Math Methods Med, 2013, 2013(1):365410.

6. Sun J, Jing R, Wang Y, et al. PPM-Dom: a novel method   for domain position prediction.[J]. Computational Biology & Chemistry,   2013, 47(6):8-15.

7. Jiao Lin, Qifan Kuang, Yizhou Li, et al. Prediction of   adverse drug reactions by a network based external link prediction method[J].   Analytical Methods, 2013, 5(21):6120-6127.

 

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