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卢红梅

领域:新材料产业 学校:中南大学职称:教授

光谱分析与化学计量学...

具体了解该专家信息,请致电:027-87555799 邮箱 haizhi@uipplus.com

教育背景

工作经历

2001-至今,中南大学化学化工学院从事分析化学工作 2017年,受欧盟ERASMUS项目资助,在葡萄牙Algarve大学和Cadiz大学进行讲学 2014年,受欧盟ERASMUS项目资助,在挪威Bergen大学、西班牙Barcelona大学进行讲学 2013年,受欧盟ERASMUS项目资助,在西班牙Cadiz大学进行讲学 2006-2007年,受英国生物技术和生命科学研究委员会(BBSRC)中国伙伴基金(China Partner Award)的资助,于英国Manchester大学的多学科生物中心代谢组学研 究小组进行访问学习

项目课题经历

主持承担科研项目20余项,其中国家自然科学基金6项。受英国生物技术和生命科学研究委员会(BBSRC)及欧盟Erasmus Mundus计划的资助,于英国Manchester大学、西班牙Cadiz大学和Barcenola大学、葡萄牙Algarve大学、挪威Bergen大学进行访问与讲学

论文、成果、著作等

[1] Hongchao Ji, Yamei Xu, Hongmei Lu, Zhimin Zhang. Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification [J]. Anal. Chem., 2019,
[2] Ming Wen,Peisheng Cong,Zhimin Zhang, Hongmei Lu,Tonghua Li. DeepMirTar: a deep-learning approach for predicting human miRNA targets, Bioinformatics, 2018,
[3] Hongchao Ji, Zhimin Zhang, Hongmei Lu. TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis [J]. Metabolomics, 2018, 14: 68
[4] Ji, Hongchao Zeng, Fanjuan Xu, Yamei Lu, Hongmei Zhang, Zhimin. KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms [J]. Analytical Chemistry, 2017, 89: 7631-7640
[5] Wen, Ming Zhang, Zhimin Niu, Shaoyu Sha, Haozhi Yang, Ruihan Yun, Yonghuan Lu, Hongmei. Deep-Learning-Based Drug–Target Interaction Prediction [J]. Journal of Proteome Research, 2017, 16(4): 1401-1409
[6] Zhou, Xinyi Wang, Yang Yun, Yonghuan Xia, Zian Lu, Hongmei Luo, Jiekun Liang, Yizeng. A potential tool for diagnosis of male infertility: Plasma metabolomics based on GC–MS [J]. Talanta, 2016, 147: 82-89
[7] Dai L, Gonçalves CMV, Lin Z, Huang J, Lu H, Yi L, Liang Y, Wang D, An D. Exploring metabolic syndrome serum free fatty acid profiles based on GC–SIM–MS combined with random forests and canonical correlation analysis [J]. Talanta, 2015, 135: 108-114
[8] Yun Y-H, Liang F, Deng B-C, Lai G-B, Gonçalves CMV, Lu H-M, Yan J, Huang X, Yi L-Z, Liang Y-Z. nformative metabolites identification by variable importance analysis based on random variable combination [J]. Metabolomics, 2015, 11: 1539-1551
[9] Dong, Nai-Ping Liang, Yi-Zeng Xu, Qing-Song Mok, Daniel K. W. Yi, Lun-Zhao Lu, Hong-Mei He, Min Fan,. Prediction of Peptide fragment ion mass spectra by data mining techniques [J]. Analytical Chemistry, 86(15): 7446-7454
[10] Dong N, Liang Y, Yi L, Lu H. Investigation of Scrambled Ions in Tandem Mass Spectra. Part. 2. On The Influence of the Ions On Peptide Identification. [J]. J Am Soc Mass Spectrom, 2013, 24: 857-867
[11] Wen M, Deng B-C, Cao D-S, Yun Y-H, Yang R-H, Lu H-M, Liang Y-Z. The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis [J]. Analyst, 2016, 141: 5586-5597
[12] Lin Z, Gonçalves C, Dai L, Lu H-M, huang J-h, Ji H-c, Yi L-z, Liang Y-z. Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest algorithm [J]. Analytica Chimica Acta, 2014, 827: 22-27
[13] Zhang Z-M, Tong X, Peng Y, Ma P, Zhang M-J, Lu H-M, Chen X-Q, Liang Y-Z. Multiscale peak detection in wavelet space [J]. Analyst, 2015, 140: 7955 - 7964
[14] Deng B-C, Yun Y-H, Cao D-S, Yin Y-L, Wang W-T, Lu H-M, Luo Q-Y, Liang Y-Z. A bootstrapping soft shrinkage approach for variable selection in chemical modeling Analytica Chimica Acta, 2016, 908: 63-74
[15] Yun Y-H, Wang W-T, Tan M-L, Liang Y, Li H-D, Cao D-S, Lu H-M, Xu Q-S. A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration [J]. Analytica Chimica Acta, 2014, 807: 36-43
[16] Zhang L, Tan B, Zeng M, Lu H, Liang Y. Establishment of reliable mass spectra and retention indices library: identification of fatty acids in human plasma without authentic standards [J]. Talanta, 2012, 88: 311-317
[17] Zhang Z-M, Liang Y-Z, Lu H-M, Tan B-B, Xu X-N, Ferro M. Multiscale peak alignment for chromatographic datasets [J]. Journal of Chromatography, 2012, 1223: 93–106
[18] Huang J, Yan J, Wu Q, Ferro MD, Lu H, Xu Q, Liang Y. Selective of informative metabolites using random forests based on model population analysis [J]. Talanta, 2013, 117: 549-555
[19] Huang J, Xie H-L, Yan J, Lu H, Xu Q-S, Liang Y-Z. Using random forest to classify T-cell epitopes based on amino acid properties and molecular features. [J]. Analytica Chimica Acta, 2013, 804: :70-75
[20] Huang J-H, Wen M, Tang L-J, Xie H-L, Fu L, Liang Y-Z, Lu H-M. Using random forest to classify linear B-cell epitopes based on amino acid properties and molecular features [J]. Biochimie, 2014, 103C: 1-6

专利、著作版权等

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