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For: Chen L, Zhang YH, Zheng M, Huang T, Cai YD. Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds. Mol Genet Genomics 2016;291:2065-79. [PMID: 27530612 DOI: 10.1007/s00438-016-1240-x] [Cited by in Crossref: 48] [Cited by in F6Publishing: 51] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Nair MM, Kumar SHK, Jyothsna S, Sundaram KT, Manjunatha C, Sivasamy M, Alagu M. Stem and leaf rust–induced miRNAome in bread wheat near-isogenic lines and their comparative analysis. Appl Microbiol Biotechnol 2022. [DOI: 10.1007/s00253-022-12268-4] [Reference Citation Analysis]
2 Xu S, Chen Z, Ge L, Ma C, He Q, Liu W, Zhang L, Zhou L. Identification of potential biomarkers and pathogenesis in neutrophil-predominant severe asthma: A comprehensive bioinformatics analysis. Medicine 2022;101:e30661. [DOI: 10.1097/md.0000000000030661] [Reference Citation Analysis]
3 Xu H, Chen X, Niu X, Pu J. CCDC103: A Novel Biomarker with Potential Prognostic in Glioma.. [DOI: 10.21203/rs.3.rs-2026652/v1] [Reference Citation Analysis]
4 Lv Y, Li X, Zhang H, Zou F, Shen B. CircRNA expression profiles in deltamethrin-susceptible and -resistant Culex pipiens pallens (Diptera: Culicidae). Comp Biochem Physiol B Biochem Mol Biol 2022;261:110750. [PMID: 35513264 DOI: 10.1016/j.cbpb.2022.110750] [Reference Citation Analysis]
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6 Liang F, Fu X, Ding S, Li L. Use of a Network-Based Method to Identify Latent Genes Associated with Hearing Loss in Children. Front Cell Dev Biol 2021;9:783500. [PMID: 34912812 DOI: 10.3389/fcell.2021.783500] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Pansari P, Manu Marg, Alwar- 301 001, Rajasthan, India. COMPUTATIONAL APPROACHES FOR DRUG DISCOVERY FROM MEDICINAL PLANTS IN THE ERA OF DATA DRIVEN RESEARCH. IND DRU 2021;58:7-23. [DOI: 10.53879/id.58.08.12930] [Reference Citation Analysis]
8 Thieme S, Walther D. Biclique extension as an effective approach to predict novel interaction partners in metabolic compound-protein interaction networks.. [DOI: 10.1101/2021.09.23.461460] [Reference Citation Analysis]
9 Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, Maojo V, Pazos A, Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J 2021;19:4538-58. [PMID: 34471498 DOI: 10.1016/j.csbj.2021.08.011] [Cited by in Crossref: 22] [Cited by in F6Publishing: 19] [Article Influence: 22.0] [Reference Citation Analysis]
10 Gu C, Shi X, Dang X, Chen J, Chen C, Chen Y, Pan X, Huang T. Identification of Common Genes and Pathways in Eight Fibrosis Diseases. Front Genet 2020;11:627396. [PMID: 33519923 DOI: 10.3389/fgene.2020.627396] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 15.0] [Reference Citation Analysis]
11 Ren X, Wang S, Huang T. Decipher the connections between proteins and phenotypes. Biochim Biophys Acta Proteins Proteom 2020;1868:140503. [PMID: 32707349 DOI: 10.1016/j.bbapap.2020.140503] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
12 Zhu L, Huang P, Zhu R, Guan F, Guo W. Extracting Explicable Rules for the Identification of Compound–Protein Interactions. IEEE Access 2020;8:70005-70012. [DOI: 10.1109/access.2020.2984824] [Reference Citation Analysis]
13 Liu M, Liu G. Prediction of Citrullination Sites on the Basis of mRMR Method and SNN. Comb Chem High Throughput Screen 2019;22:705-15. [PMID: 31782357 DOI: 10.2174/1386207322666191129113508] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
14 Zhang GL, Pan LL, Huang T, Wang JH. The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing. J Cancer 2019;10:5883-90. [PMID: 31737124 DOI: 10.7150/jca.32267] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 5.7] [Reference Citation Analysis]
15 Chen L, Zhang YH, Huang G, Pan X, Huang T, Cai YD. Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms. Gene Ther 2019;26:465-78. [PMID: 31455874 DOI: 10.1038/s41434-019-0099-y] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
16 Yuan F, Pan X, Chen L, Zhang YH, Huang T, Cai YD. Analysis of Protein-Protein Functional Associations by Using Gene Ontology and KEGG Pathway. Biomed Res Int 2019;2019:4963289. [PMID: 31396531 DOI: 10.1155/2019/4963289] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 3.3] [Reference Citation Analysis]
17 Lu S, Zhu ZG, Lu WC. Inferring novel genes related to colorectal cancer via random walk with restart algorithm. Gene Ther 2019;26:373-85. [PMID: 31308477 DOI: 10.1038/s41434-019-0090-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
18 Zhang Y, Dong D, Li D, Lu L, Li J, Zhang Y, Chen L. Computational Method for the Identification of Molecular Metabolites Involved in Cereal Hull Color Variations. CCHTS 2019;21:760-70. [DOI: 10.2174/1386207322666190129105441] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
19 Zamanian Azodi M, Rezaei Tavirani M, Rezaei Tavirani M. Compound-Protein Interaction Analysis in Condition Following Cardiac Arrest. Galen Med J 2018;7:e1380. [PMID: 34466450 DOI: 10.22086/gmj.v0i0.1380] [Reference Citation Analysis]
20 Chen L, Pan X, Zhang YH, Liu M, Huang T, Cai YD. Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network. Comput Struct Biotechnol J 2019;17:49-60. [PMID: 30595815 DOI: 10.1016/j.csbj.2018.12.002] [Cited by in Crossref: 29] [Cited by in F6Publishing: 30] [Article Influence: 7.3] [Reference Citation Analysis]
21 Chen L, Zhang YH, Pan X, Liu M, Wang S, Huang T, Cai YD. Tissue Expression Difference between mRNAs and lncRNAs. Int J Mol Sci 2018;19:E3416. [PMID: 30384456 DOI: 10.3390/ijms19113416] [Cited by in Crossref: 38] [Cited by in F6Publishing: 40] [Article Influence: 9.5] [Reference Citation Analysis]
22 Lu S, Zhao K, Wang X, Liu H, Ainiwaer X, Xu Y, Ye M. Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis. Front Genet 2018;9:425. [PMID: 30349554 DOI: 10.3389/fgene.2018.00425] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
23 Luo B, Gu YY, Wang XD, Chen G, Peng ZG. Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database. Pathol Res Pract 2018;214:1854-67. [PMID: 30244948 DOI: 10.1016/j.prp.2018.09.013] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 2.3] [Reference Citation Analysis]
24 Li J, Chen L, Zhang YH, Kong X, Huang T, Cai YD. A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes. Genes (Basel) 2018;9:E449. [PMID: 30205473 DOI: 10.3390/genes9090449] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
25 Li J, Lu L, Zhang YH, Liu M, Chen L, Huang T, Cai YD. Identification of synthetic lethality based on a functional network by using machine learning algorithms. J Cell Biochem 2019;120:405-16. [PMID: 30125975 DOI: 10.1002/jcb.27395] [Cited by in Crossref: 43] [Cited by in F6Publishing: 44] [Article Influence: 10.8] [Reference Citation Analysis]
26 Yuan F, Lu L, Zhang Y, Wang S, Cai YD. Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method. Math Biosci 2018;304:1-8. [PMID: 30086268 DOI: 10.1016/j.mbs.2018.08.001] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 4.3] [Reference Citation Analysis]
27 Cai L, Huang T, Su J, Zhang X, Chen W, Zhang F, He L, Chou KC. Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia. Mol Ther Nucleic Acids 2018;12:433-42. [PMID: 30195780 DOI: 10.1016/j.omtn.2018.05.026] [Cited by in Crossref: 54] [Cited by in F6Publishing: 60] [Article Influence: 13.5] [Reference Citation Analysis]
28 Chen L, Zhang YH, Zhang Z, Huang T, Cai YD. Inferring Novel Tumor Suppressor Genes with a Protein-Protein Interaction Network and Network Diffusion Algorithms. Mol Ther Methods Clin Dev 2018;10:57-67. [PMID: 30069494 DOI: 10.1016/j.omtm.2018.06.007] [Cited by in Crossref: 27] [Cited by in F6Publishing: 28] [Article Influence: 6.8] [Reference Citation Analysis]
29 Yi F, Li L, Xu LJ, Meng H, Dong YM, Liu HB, Xiao PG. In silico approach in reveal traditional medicine plants pharmacological material basis. Chin Med 2018;13:33. [PMID: 29946351 DOI: 10.1186/s13020-018-0190-0] [Cited by in Crossref: 42] [Cited by in F6Publishing: 42] [Article Influence: 10.5] [Reference Citation Analysis]
30 Pan X, Hu X, Zhang YH, Feng K, Wang SP, Chen L, Huang T, Cai YD. Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection. Genes (Basel) 2018;9:E208. [PMID: 29649131 DOI: 10.3390/genes9040208] [Cited by in Crossref: 30] [Cited by in F6Publishing: 31] [Article Influence: 7.5] [Reference Citation Analysis]
31 Luo Y, Yan Y, Zhang S, Li Z. Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV. Biomed Res Int 2018;2018:8406857. [PMID: 29850576 DOI: 10.1155/2018/8406857] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
32 Wang D, Li JR, Zhang YH, Chen L, Huang T, Cai YD. Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms. Genes (Basel) 2018;9:E155. [PMID: 29534550 DOI: 10.3390/genes9030155] [Cited by in Crossref: 44] [Cited by in F6Publishing: 48] [Article Influence: 11.0] [Reference Citation Analysis]
33 Wang S, Cai Y. Identification of the functional alteration signatures across different cancer types with support vector machine and feature analysis. Biochim Biophys Acta Mol Basis Dis 2018;1864:2218-27. [PMID: 29277326 DOI: 10.1016/j.bbadis.2017.12.026] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 2.8] [Reference Citation Analysis]
34 Zhang YH, Hu Y, Zhang Y, Hu LD, Kong X. Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine. Biochim Biophys Acta Mol Basis Dis 2018;1864:2255-65. [PMID: 29241664 DOI: 10.1016/j.bbadis.2017.12.003] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
35 Zhang J, Suo Y, Liu M, Xu X. Identification of genes related to proliferative diabetic retinopathy through RWR algorithm based on protein-protein interaction network. Biochim Biophys Acta Mol Basis Dis 2018;1864:2369-75. [PMID: 29237571 DOI: 10.1016/j.bbadis.2017.11.017] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 4.0] [Reference Citation Analysis]
36 Li Z, Jiang C, Li X, Wu WKK, Chen X, Zhu S, Ye C, Chan MTV, Qian W. Circulating microRNA signature of steroid-induced osteonecrosis of the femoral head. Cell Prolif 2018;51. [PMID: 29205600 DOI: 10.1111/cpr.12418] [Cited by in Crossref: 13] [Cited by in F6Publishing: 18] [Article Influence: 2.6] [Reference Citation Analysis]
37 Yuan F, Lu W. Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein-protein interaction network. Biochim Biophys Acta Mol Basis Dis 2018;1864:2284-93. [PMID: 29197663 DOI: 10.1016/j.bbadis.2017.11.018] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.0] [Reference Citation Analysis]
38 Zhu L, Su F, Xu Y, Zou Q. Network-based method for mining novel HPV infection related genes using random walk with restart algorithm. Biochim Biophys Acta Mol Basis Dis 2018;1864:2376-83. [PMID: 29197659 DOI: 10.1016/j.bbadis.2017.11.021] [Cited by in Crossref: 19] [Cited by in F6Publishing: 22] [Article Influence: 3.8] [Reference Citation Analysis]
39 Li J, Huang T. Predicting and analyzing early wake-up associated gene expressions by integrating GWAS and eQTL studies. Biochim Biophys Acta Mol Basis Dis 2018;1864:2241-6. [PMID: 29109033 DOI: 10.1016/j.bbadis.2017.10.036] [Cited by in Crossref: 27] [Cited by in F6Publishing: 32] [Article Influence: 5.4] [Reference Citation Analysis]
40 Li J, Chen L, Wang S, Zhang Y, Kong X, Huang T, Cai YD. A computational method using the random walk with restart algorithm for identifying novel epigenetic factors. Mol Genet Genomics 2018;293:293-301. [PMID: 28932904 DOI: 10.1007/s00438-017-1374-5] [Cited by in Crossref: 22] [Cited by in F6Publishing: 18] [Article Influence: 4.4] [Reference Citation Analysis]
41 Zhang YH, Huang T, Chen L, Xu Y, Hu Y, Hu LD, Cai Y, Kong X. Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets. Oncotarget 2017;8:87494-511. [PMID: 29152097 DOI: 10.18632/oncotarget.20903] [Cited by in Crossref: 37] [Cited by in F6Publishing: 39] [Article Influence: 7.4] [Reference Citation Analysis]
42 Chen L, Zhang Y, Huang G, Pan X, Wang S, Huang T, Cai Y. Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection. Mol Genet Genomics 2018;293:137-49. [DOI: 10.1007/s00438-017-1372-7] [Cited by in Crossref: 45] [Cited by in F6Publishing: 46] [Article Influence: 9.0] [Reference Citation Analysis]
43 Chen L, Zhang YH, Wang S, Zhang Y, Huang T, Cai YD. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PLoS One 2017;12:e0184129. [PMID: 28873455 DOI: 10.1371/journal.pone.0184129] [Cited by in Crossref: 85] [Cited by in F6Publishing: 100] [Article Influence: 17.0] [Reference Citation Analysis]
44 Li L, Wang Y, An L, Kong X, Huang T. A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease. PLoS One 2017;12:e0182592. [PMID: 28787010 DOI: 10.1371/journal.pone.0182592] [Cited by in Crossref: 25] [Cited by in F6Publishing: 27] [Article Influence: 5.0] [Reference Citation Analysis]
45 Lu S, Yan Y, Li Z, Chen L, Yang J, Zhang Y, Wang S, Liu L. Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein-Protein Interaction Network. Int J Mol Sci 2017;18:E1045. [PMID: 28505077 DOI: 10.3390/ijms18051045] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 3.0] [Reference Citation Analysis]
46 Zhang Y, Dai L, Liu Y, Zhang Y, Wang S. Identifying novel fruit-related genes in Arabidopsis thaliana based on the random walk with restart algorithm. PLoS One 2017;12:e0177017. [PMID: 28472169 DOI: 10.1371/journal.pone.0177017] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 1.8] [Reference Citation Analysis]
47 Chen L, Zhang YH, Lu G, Huang T, Cai YD. Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways. Artif Intell Med 2017;76:27-36. [PMID: 28363286 DOI: 10.1016/j.artmed.2017.02.001] [Cited by in Crossref: 81] [Cited by in F6Publishing: 72] [Article Influence: 16.2] [Reference Citation Analysis]
48 Guo W, Shang DM, Cao JH, Feng K, He YC, Jiang Y, Wang S, Gao YF. Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm. Biomed Res Int 2017;2017:6132436. [PMID: 28255556 DOI: 10.1155/2017/6132436] [Cited by in Crossref: 11] [Cited by in F6Publishing: 14] [Article Influence: 2.2] [Reference Citation Analysis]
49 Yin H, Wang S, Zhang YH, Cai YD, Liu H. Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer. Biomed Res Int 2016;2016:7861274. [PMID: 27957501 DOI: 10.1155/2016/7861274] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 1.3] [Reference Citation Analysis]