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For: Li X, Li W, Zeng M, Zheng R, Li M. Network-based methods for predicting essential genes or proteins: a survey. Briefings in Bioinformatics 2020;21:566-83. [DOI: 10.1093/bib/bbz017] [Cited by in Crossref: 50] [Cited by in F6Publishing: 55] [Article Influence: 16.7] [Reference Citation Analysis]
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2 Bonner S, Barrett IP, Ye C, Swiers R, Engkvist O, Bender A, Hoyt CT, Hamilton WL. A review of biomedical datasets relating to drug discovery: a knowledge graph perspective. Brief Bioinform 2022:bbac404. [PMID: 36151740 DOI: 10.1093/bib/bbac404] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
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5 Khan SH, Tayara H, Chong KT. ProB-Site: Protein Binding Site Prediction Using Local Features. Cells 2022;11:2117. [PMID: 35805201 DOI: 10.3390/cells11132117] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Zhu Y, Zhang H, Yang Y, Zhang C, Ou-Yang L, Bai L, Deng M, Yi M, Liu S, Wang C. Discovery of pan-cancer related genes via integrative network analysis. Brief Funct Genomics 2022:elac012. [PMID: 35760070 DOI: 10.1093/bfgp/elac012] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Mosharaf MP, Reza MS, Gov E, Mahumud RA, Mollah MNH. Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis. Vaccines 2022;10:771. [DOI: 10.3390/vaccines10050771] [Reference Citation Analysis]
8 Mahbub S, Bayzid MS. EGRET: edge aggregated graph attention networks and transfer learning improve protein-protein interaction site prediction. Brief Bioinform 2022:bbab578. [PMID: 35106547 DOI: 10.1093/bib/bbab578] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
9 Zeng M, Wang N, Wu Y, Li Y, Wu F, Li M. Improving human essential protein prediction using only protein sequences via ensemble learning. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021. [DOI: 10.1109/bibm52615.2021.9669606] [Reference Citation Analysis]
10 Her HL, Lin PT, Wu YW. PangenomeNet: a pan-genome-based network reveals functional modules on antimicrobial resistome for Escherichia coli strains. BMC Bioinformatics 2021;22:548. [PMID: 34758735 DOI: 10.1186/s12859-021-04459-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Liu Y, Wei X, Chen W, Hu L, He Z. A graph-traversal approach to identify influential nodes in a network. Patterns 2021;2:100321. [DOI: 10.1016/j.patter.2021.100321] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Zhang H, Zhuge C, Jia J, Shi B, Wang W. Green travel mobility of dockless bike-sharing based on trip data in big cities: A spatial network analysis. Journal of Cleaner Production 2021;313:127930. [DOI: 10.1016/j.jclepro.2021.127930] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
13 Kumar N, Mishra B, Athar M, Mukhtar S. Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC. Methods Mol Biol 2021;2328:171-82. [PMID: 34251625 DOI: 10.1007/978-1-0716-1534-8_10] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
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15 Yan Y, Jiang F, Zhang X, Tian T. Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data. Symmetry 2021;13:1097. [DOI: 10.3390/sym13061097] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Daniels MW, Dvorkin D, Powers RK, Kechris K. Semi-Supervised Learning Using Hierarchical Mixture Models: Gene Essentiality Case Study. MCA 2021;26:40. [DOI: 10.3390/mca26020040] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Estrada E. Informational cost and networks navigability. Applied Mathematics and Computation 2021;397:125914. [DOI: 10.1016/j.amc.2020.125914] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
18 Schonfeld E, Vendrow E, Vendrow J, Schonfeld E. On the relation of gene essentiality to intron structure: a computational and deep learning approach. Life Sci Alliance 2021;4:e202000951. [PMID: 33906938 DOI: 10.26508/lsa.202000951] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Xiang J, Zhang J, Zheng R, Li X, Li M. NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction. Brief Bioinform 2021:bbab080. [PMID: 33866352 DOI: 10.1093/bib/bbab080] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 9.0] [Reference Citation Analysis]
20 Zhou Q, Qi S, Ren C. Gene essentiality prediction based on chaos game representation and spiking neural networks. Chaos, Solitons & Fractals 2021;144:110649. [DOI: 10.1016/j.chaos.2021.110649] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
21 Lv Z, Cui F, Zou Q, Zhang L, Xu L. Anticancer peptides prediction with deep representation learning features. Brief Bioinform 2021:bbab008. [PMID: 33529337 DOI: 10.1093/bib/bbab008] [Cited by in Crossref: 30] [Cited by in F6Publishing: 33] [Article Influence: 30.0] [Reference Citation Analysis]
22 Meng X, Li W, Peng X, Li Y, Li M. Protein interaction networks: centrality, modularity, dynamics, and applications. Front Comput Sci 2021;15. [DOI: 10.1007/s11704-020-8179-0] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 10.0] [Reference Citation Analysis]
23 Nandi S, Ganguli P, Sarkar RR. Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy. PLoS One 2020;15:e0242943. [PMID: 33253254 DOI: 10.1371/journal.pone.0242943] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
24 Manakkadu S, Dutta S, Thangiah SR. Profiling of Disease-Associated Proteins Neighborhood Networks. 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE) 2020. [DOI: 10.1109/bigdatase50710.2020.00009] [Reference Citation Analysis]
25 Liu X, Maiorino E, Halu A, Glass K, Prasad RB, Loscalzo J, Gao J, Sharma A. Robustness and lethality in multilayer biological molecular networks. Nat Commun 2020;11:6043. [PMID: 33247151 DOI: 10.1038/s41467-020-19841-3] [Cited by in Crossref: 28] [Cited by in F6Publishing: 30] [Article Influence: 14.0] [Reference Citation Analysis]
26 Mahbub S, Bayzid MS. EGRET: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction.. [DOI: 10.1101/2020.11.07.372466] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
27 Mazandu GK, Hooper C, Opap K, Makinde F, Nembaware V, Thomford NE, Chimusa ER, Wonkam A, Mulder NJ. IHP-PING-generating integrated human protein-protein interaction networks on-the-fly. Brief Bioinform 2021;22:bbaa277. [PMID: 33129201 DOI: 10.1093/bib/bbaa277] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
28 Liu R, Mancuso CA, Yannakopoulos A, Johnson KA, Krishnan A. Supervised learning is an accurate method for network-based gene classification. Bioinformatics 2020;36:3457-65. [PMID: 32129827 DOI: 10.1093/bioinformatics/btaa150] [Cited by in Crossref: 9] [Cited by in F6Publishing: 12] [Article Influence: 4.5] [Reference Citation Analysis]
29 Zhang X, Xiao W, Xiao W. DeepHE: Accurately predicting human essential genes based on deep learning. PLoS Comput Biol 2020;16:e1008229. [PMID: 32936825 DOI: 10.1371/journal.pcbi.1008229] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 6.0] [Reference Citation Analysis]
30 Khorsand B, Savadi A, Naghibzadeh M. Comprehensive host-pathogen protein-protein interaction network analysis. BMC Bioinformatics 2020;21:400. [PMID: 32912135 DOI: 10.1186/s12859-020-03706-z] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
31 Xiang J, Zhang NR, Zhang JS, Lv XY, Li M. PrGeFNE: Predicting disease-related genes by fast network embedding. Methods 2021;192:3-12. [PMID: 32610158 DOI: 10.1016/j.ymeth.2020.06.015] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 7.0] [Reference Citation Analysis]
32 Zhao T, Hu Y, Zang T, Wang Y. Identifying Protein Biomarkers in Blood for Alzheimer's Disease. Front Cell Dev Biol 2020;8:472. [PMID: 32626709 DOI: 10.3389/fcell.2020.00472] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
33 Ivanov S, Lagunin A, Filimonov D, Tarasova O. Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction. Front Microbiol 2020;11:1314. [PMID: 32625189 DOI: 10.3389/fmicb.2020.01314] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
34 Yan W, Liu X, Wang Y, Han S, Wang F, Liu X, Xiao F, Hu G. Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling. Front Pharmacol 2020;11:534. [PMID: 32425783 DOI: 10.3389/fphar.2020.00534] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
35 Zhang X, Xiao W, Xiao W. DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning.. [DOI: 10.1101/2020.02.14.950048] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
36 Zhao B, Han X, Liu X, Luo Y, Hu S, Zhang Z, Wang L. A Novel Method to Predict Essential Proteins Based on Diffusion Distance Networks. IEEE Access 2020;8:29385-29394. [DOI: 10.1109/access.2020.2972922] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
37 Zeng M, Li M, Wu FX, Li Y, Pan Y. DeepEP: a deep learning framework for identifying essential proteins. BMC Bioinformatics 2019;20:506. [PMID: 31787076 DOI: 10.1186/s12859-019-3076-y] [Cited by in Crossref: 18] [Cited by in F6Publishing: 20] [Article Influence: 6.0] [Reference Citation Analysis]
38 Li X, Xiang J, Hu X, Wu F, Li M. DualRank: multiplex network-based dual ranking for heterogeneous complex disease analysis. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2019. [DOI: 10.1109/bibm47256.2019.8983050] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
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40 Liu R, Mancuso CA, Yannakopoulos A, Johnson KA, Krishnan A. Supervised-learning is an accurate method for network-based gene classification.. [DOI: 10.1101/721423] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
41 Wen QF, Liu S, Dong C, Guo HX, Gao YZ, Guo FB. Geptop 2.0: An Updated, More Precise, and Faster Geptop Server for Identification of Prokaryotic Essential Genes. Front Microbiol 2019;10:1236. [PMID: 31214154 DOI: 10.3389/fmicb.2019.01236] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 6.3] [Reference Citation Analysis]