picture
 
李梦龙
职称与职务
教 授
专      业
分析化学
电      话
E-Mail
简  历
1978年-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年-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,microRNA与mRNA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. 2016,638575.
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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