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The prediction of protein–ligand unbinding for modern drug discovery Journal article
Zhang, Qianqian, Zhao, Nannan, Meng, Xiaoxiao, Yu, Fansen, Yao, Xiaojun, Liu, Huanxiang. The prediction of protein–ligand unbinding for modern drug discovery[J]. Expert Opinion on Drug Discovery, 2021, 17(2), 191-205.
Authors:  Zhang, Qianqian;  Zhao, Nannan;  Meng, Xiaoxiao;  Yu, Fansen;  Yao, Xiaojun; et al.
Favorite | TC[WOS]:11 TC[Scopus]:13  IF:6.0/6.6 | Submit date：2023/01/30
Binding Free Energy  Dissociation Rate Constant  Enhanced Sampling Methods  Machine Learning  Molecular Dynamic Simulation  Protein–ligand Unbinding  Residence Time  Unbinding Pathways  
Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study Journal article
Haijing Niu, Zhaojun Zhu, Mengjing Wan, Xuanyu Li, Zhen Yuan, Yu Sun, Ying Han. Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study[J]. Neurophotonics, 2019, 6(2), 025010.
Authors:  Haijing Niu;  Zhaojun Zhu;  Mengjing Wan;  Xuanyu Li;  Zhen Yuan; et al.
Adobe PDF | Favorite | TC[WOS]:32 TC[Scopus]:32 | Submit date：2022/08/21
CommunicAtion WithIn The BraIn Is Highly Dynamic. AlzheI.e.’s dIseAse (Ad) ExhibIts Dynamic.progression COrrespondIng To a DeclIne In MemOry And Cognition. HoWever, Little Is Known Of wheTher BraIn Dynamic. Are dIsrupted In Ad And Its Prodromal Stage, Mild CognitI.e.impairment (Mci). FOr Our Study, We AcquI.e. High samplIng RAte Functional near-InfrAred Spectroscopy imagIng DAta At Rest From The EntI.e.cOrtex Of 23 pAtients With Ad Dementia, 25 pAtients With Amnestic Mild CognitI.e.impairment (aMci), And 30 age-mAtched Healthy Controls (Hcs). slidIng-wIndow cOrrelAtion And K-means clusterIng Analyses Were Used To Construct Dynamic.Functional Connectivity (Fc) Maps FOr Each Participant. We dIscovered thAt The BraIn’s Dynamic.Fc Variability Strength (q) Significantly IncreAsed In Both aMci And Ad Group As compAred To Hcs. usIng The q Value As a meAsurement, The clAssificAtion perFOrmance ExhibI.e. a Good poWer In differentiAtIng aMci [Area Under The Curve (Auc ¼ 82.5%)] Or Ad (Auc ¼ 86.4%) From Hcs. furThermOre, We Identified Two abnOrmal BraIn Fc stAtes In The Ad Group, Of Which The Occurrence Frequency (f) ExhibI.e. a Significant decreAse FOr The First-level Fc stAte (stAte 1) And a Significant IncreAse FOr The Second-level Fc stAte (stAte 2). We Also Found thAt The abnOrmal f In These Two stAtes Significantly cOrrelAted With The CognitI.e.impairment In pAtients. These fIndIngs provI.e.The First EvI.e.ce To demonstRAte The dIsruptions Of Dynamic.BraIn Connectivity In aMci And Ad And Extend The trAditional stAtic (I.e., tI.e.averaged) Fc fIndIngs In The dIseAse (I.e., dIsconnection Syndrome) And Thus provI.e.Insights InTo UnderstAndIng The pAthophysiological mechanIsms occurrIng In aMci And Ad.  
The Generalization Ability of SVM Classification Based on Markov Sampling Journal article
Jie Xu, Yuan Yan Tang,, Bin Zou, Zongben Xu, Luoqing Li, Yang Lu, Baochang Zhang. The Generalization Ability of SVM Classification Based on Markov Sampling[J]. IEEE Transactions on Cybernetics, 2015, 45(6), 1169-1179.
Authors:  Jie Xu;  Yuan Yan Tang,;  Bin Zou;  Zongben Xu;  Luoqing Li; et al.
Favorite | TC[WOS]:41 TC[Scopus]:47 | Submit date：2019/02/11
Generalization Ability  Learning Rate  Markov Sampling  Support Vector Machine Classification (Svmc)  
Image-based real time walkthrough Journal article
Zhang Y.-C., Wu E.-H., Wu E.-H.. Image-based real time walkthrough[J]. Ruan Jian Xue Bao/Journal of Software, 2002, 13(9), 1796-1803.
Authors:  Zhang Y.-C.;  Wu E.-H.;  Wu E.-H.
Favorite |  | Submit date：2019/04/04
Image-based rendering  Real-time walkthrough  Sampling rate  Texture mapping  
Plane-based warping Journal article
Zhang Y.-C., Wu E.-H.. Plane-based warping[J]. Ruan Jian Xue Bao/Journal of Software, 2002, 13(7), 1242-1249.
Authors:  Zhang Y.-C.;  Wu E.-H.
Favorite |  | Submit date：2019/02/13
Backward-warping  Forward-warping  Image-based rendering  Sampling rate