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For: Bi XA, Liu Y, Xie Y, Hu X, Jiang Q. Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment. Bioinformatics 2020;36:2561-8. [PMID: 31971559 DOI: 10.1093/bioinformatics/btz967] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 7.5] [Reference Citation Analysis]
Number Citing Articles
1 Wang Y, Fu Y, Luo X. Identification of Pathogenetic Brain Regions via Neuroimaging Data for Diagnosis of Autism Spectrum Disorders. Front Neurosci 2022;16:900330. [DOI: 10.3389/fnins.2022.900330] [Reference Citation Analysis]
2 Wu J, Cao Y, Li M, Li B, Jia X, Cao L. Altered intrinsic brain activity in patients with CSF1R-related leukoencephalopathy. Brain Imaging Behav 2022. [PMID: 35389179 DOI: 10.1007/s11682-022-00646-5] [Reference Citation Analysis]
3 Meng X, Wu Y, Liu W, Wang Y, Xu Z, Jiao Z. Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine. Front Neuroinform 2022;16:856295. [DOI: 10.3389/fninf.2022.856295] [Reference Citation Analysis]
4 Wang JH, Zhao XL, Guo ZW, Yan P, Gao X, Shen Y, Chen YP. A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants. Environ Res 2022;211:113054. [PMID: 35276189 DOI: 10.1016/j.envres.2022.113054] [Reference Citation Analysis]
5 Zhang Y, Xi Z, Zheng J, Shi H, Jiao Z. GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease. Front Aging Neurosci 2022;14:834331. [DOI: 10.3389/fnagi.2022.834331] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Zhao Z, Yang W, Zhai Y, Liang Y, Zhao Y. Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm. Front Genet 2022;12:821996. [DOI: 10.3389/fgene.2021.821996] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
7 Guo X, Zhou W, Yu Y, Cai Y, Zhang Y, Du A, Lu Q, Ding Y, Li C. Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease. Front Physiol 2021;12:790086. [PMID: 34966294 DOI: 10.3389/fphys.2021.790086] [Reference Citation Analysis]
8 Jiao Z, Gao P, Ji Y, Shi H. Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators. Contrast Media Mol Imaging 2021;2021:6890024. [PMID: 34366726 DOI: 10.1155/2021/6890024] [Reference Citation Analysis]
9 Zhang Z, Cui L, Huang Y, Chen Y, Li Y, Guo Q. Changes of Regional Neural Activity Homogeneity in Preclinical Alzheimer's Disease: Compensation and Dysfunction. Front Neurosci 2021;15:646414. [PMID: 34220418 DOI: 10.3389/fnins.2021.646414] [Reference Citation Analysis]
10 Cui L, Zhang Z, Zac Lo CY, Guo Q. Local Functional MR Change Pattern and Its Association With Cognitive Function in Objectively-Defined Subtle Cognitive Decline. Front Aging Neurosci 2021;13:684918. [PMID: 34177559 DOI: 10.3389/fnagi.2021.684918] [Reference Citation Analysis]
11 Zheng X, Sun J, Lv Y, Wang M, Du X, Jia X, Ma J. Frequency-specific alterations of the resting-state BOLD signals in nocturnal enuresis: an fMRI Study. Sci Rep 2021;11:12042. [PMID: 34103549 DOI: 10.1038/s41598-021-90546-3] [Reference Citation Analysis]
12 Ji Y, Zhang Y, Shi H, Jiao Z, Wang SH, Wang C. Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification. Front Neurosci 2021;15:669345. [PMID: 33867931 DOI: 10.3389/fnins.2021.669345] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Wang Q, Li HY, Li YD, Lv YT, Ma HB, Xiang AF, Jia XZ, Liu DQ. Resting-state abnormalities in functional connectivity of the default mode network in autism spectrum disorder: a meta-analysis. Brain Imaging Behav 2021. [PMID: 33683528 DOI: 10.1007/s11682-021-00460-5] [Reference Citation Analysis]
14 Du L, Zhang J, Liu F, Wang H, Guo L, Han J, Disease Neuroimaging Initiative TA. Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis. Med Image Anal 2021;70:102003. [PMID: 33735757 DOI: 10.1016/j.media.2021.102003] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Cui L, Chen K, Huang L, Sun J, Lv Y, Jia X, Guo Q. Changes in local brain function in mild cognitive impairment due to semantic dementia. CNS Neurosci Ther 2021;27:587-602. [PMID: 33650764 DOI: 10.1111/cns.13621] [Reference Citation Analysis]
16 Guo X, Zhou W, Lu Q, Du A, Cai Y, Ding Y. Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm. Biomed Res Int 2021;2021:6627650. [PMID: 33628794 DOI: 10.1155/2021/6627650] [Reference Citation Analysis]
17 Sun JW, Fan R, Wang Q, Wang QQ, Jia XZ, Ma HB. Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification. Brain Res 2021;1757:147299. [PMID: 33516816 DOI: 10.1016/j.brainres.2021.147299] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Jiao Z, Ji Y, Zhang J, Shi H, Wang C. Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification. Front Cell Dev Biol 2020;8:610569. [PMID: 33505965 DOI: 10.3389/fcell.2020.610569] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Jiao Z, Jiao T, Zhang J, Shi H, Wu B, Zhang Y. Sparse structure deep network embedding for transforming brain functional network in early mild cognitive impairment classification. Int J Imaging Syst Technol 2021;31:1197-210. [DOI: 10.1002/ima.22531] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
20 Fu H, Cao Z, Li M, Wang S. ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding. BMC Genomics 2020;21:597. [PMID: 32859150 DOI: 10.1186/s12864-020-06978-0] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
21 Jiang F, Dong L, Dai Q. Electrical Resistivity Inversion Based on a Hybrid CCSFLA-MSVR Method. Neural Process Lett 2020;51:2871-90. [DOI: 10.1007/s11063-020-10229-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]