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[21]Hongchao Ji, Hanzi Deng, Hongmei Lu, Zhimin Zhang, Hongmei Lu, Zhimin Zhang, Predicting Molecular Fingerprint from Electron-Ionization Mass Spectrum with Deep Neural Networks.Anal Chem, 2020
[22]Zhimin Zhang, Hongmei Lu, Yamei Xu, Hongchao Ji.Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification[J].Anal. Chem., 2019
[23]Tonghua Li, Hongmei Lu, Zhimin Zhang, Peisheng Cong, Ming Wen.DeepMirTar: a deep-learning approach for predicting human miRNA targets,.Bioinformatics, 2018
[24]Hongmei Lu, Zhimin Zhang, Hongchao Ji.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.
[25]Zhimin, Hongmei Zhang, Yamei Lu, Fanjuan Xu, Hongchao Zeng, Ji.KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms[J].Analytical Chemistry, 2017, 89: 7631-7640.
[26]Hongmei, Yonghuan Lu, Ruihan Yun, Haozhi Yang, Shaoyu Sha, Zhimin Niu, Ming Zhang, Wen.Deep-Learning-Based Drug–Target Interaction Prediction[J].Journal of Proteome Research, 2017, 16 (4) : 1401-1409.
[27]Yizeng, Jiekun Liang, Hongmei Luo, Zian Lu, Yonghuan Xia, Yang Yun, Xinyi Wang, Zhou.A potential tool for diagnosis of male infertility: Plasma metabolomics based on GC–MS[J].Talanta, 2016, 147: 82-89.
[28]An D, Wang D, Liang Y, Yi L, Lu H, Huang J, Lin Z, Gon?alves CMV, Dai L.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.
[29]Liang Y-Z, Yi L-Z, Huang X, Yan J, Lu H-M, Gon?alves CMV, Lai G-B, Deng B-C, Liang F, Yun Y-H.nformative metabolites identification by variable importance analysis based on random variable combination[J].Metabolomics, 2015, 11: 1539-1551.
[30]Min Fan, Hong-Mei He, Lun-Zhao Lu, Daniel K. W. Yi, Qing-Song Mok, Yi-Zeng Xu, Nai-Ping Liang, Dong.Prediction of Peptide fragment ion mass spectra by data mining techniques[J].Analytical Chemistry, 86 (15) : 7446-7454.
[31]Lu H, Yi L, Liang Y, Dong N.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.
[32]Liang Y-Z, Lu H-M, Yang R-H, Yun Y-H, Cao D-S, Deng B-C, Wen M.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.
[33]Liang Y-z, Yi L-z, Ji H-c, huang J-h, Lu H-M, Dai L, Gon?alves C, Lin Z.Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest algorithm[J].Analytica Chimica Acta, 2014, 827: 22-27.
[34]Liang Y-Z, Chen X-Q, Lu H-M, Zhang M-J, Ma P, Peng Y, Tong X, Zhang Z-M.Multiscale peak detection in wavelet space[J].Analyst, 2015, 140: 7955 - 7964.
[35]Liang Y-Z, Luo Q-Y, Lu H-M, Wang W-T, Yin Y-L, Cao D-S, Yun Y-H, Deng B-C.A bootstrapping soft shrinkage approach for variable selection in chemical modeling.Analytica Chimica Acta, 2016, 908: 63-74.
[36]Xu Q-S, Lu H-M, Cao D-S, Li H-D, Liang Y, Tan M-L, Wang W-T, Yun Y-H.A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration[J].Analytica Chimica Acta, 2014, 807: 36-43.
[37]Liang Y, Lu H, Zeng M, Tan B, Zhang L.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.
[38]Ferro M, Xu X-N, Tan B-B, Lu H-M, Liang Y-Z, Zhang Z-M.Multiscale peak alignment for chromatographic datasets[J].Journal of Chromatography, 2012, 1223: 93–106.
[39]Liang Y, Xu Q, Lu H, Ferro MD, Wu Q, Yan J, Huang J.Selective of informative metabolites using random forests based on model population analysis[J].Talanta, 2013, 117: 549-555.
[40]Liang Y-Z, Xu Q-S, Lu H, Yan J, Xie H-L, Huang J.Using random forest to classify T-cell epitopes based on amino acid properties and molecular features[J].Analytica Chimica Acta, 2013, 804: :70-75.
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