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Journal Abstract Search
172 related items for PubMed ID: 36353604
1. APPINetwork: an R package for building and computational analysis of protein-protein interaction networks. Gosset S, Glatigny A, Gallopin M, Yi Z, Salé M, Mucchielli-Giorgi MH. PeerJ; 2022; 10():e14204. PubMed ID: 36353604 [Abstract] [Full Text] [Related]
2. ModuleRole: a tool for modulization, role determination and visualization in protein-protein interaction networks. Li G, Li M, Zhang Y, Wang D, Li R, Guimerà R, Gao JT, Zhang MQ. PLoS One; 2014; 9(5):e94608. PubMed ID: 24788790 [Abstract] [Full Text] [Related]
3. PINOT: an intuitive resource for integrating protein-protein interactions. Tomkins JE, Ferrari R, Vavouraki N, Hardy J, Lovering RC, Lewis PA, McGuffin LJ, Manzoni C. Cell Commun Signal; 2020 Jun 11; 18(1):92. PubMed ID: 32527260 [Abstract] [Full Text] [Related]
4. POINeT: protein interactome with sub-network analysis and hub prioritization. Lee SA, Chan CH, Chen TC, Yang CY, Huang KC, Tsai CH, Lai JM, Wang FS, Kao CY, Huang CY. BMC Bioinformatics; 2009 Apr 21; 10():114. PubMed ID: 19379523 [Abstract] [Full Text] [Related]
5. Constructing a robust protein-protein interaction network by integrating multiple public databases. Martha VS, Liu Z, Guo L, Su Z, Ye Y, Fang H, Ding D, Tong W, Xu X. BMC Bioinformatics; 2011 Oct 18; 12 Suppl 10(Suppl 10):S7. PubMed ID: 22165958 [Abstract] [Full Text] [Related]
6. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, Moraleda C, Rogers L, Daniels K, Green P. Cochrane Database Syst Rev; 2022 Feb 01; 2(2022):. PubMed ID: 36321557 [Abstract] [Full Text] [Related]
7. Visualization of protein interaction networks: problems and solutions. Agapito G, Guzzi PH, Cannataro M. BMC Bioinformatics; 2013 Feb 01; 14 Suppl 1(Suppl 1):S1. PubMed ID: 23368786 [Abstract] [Full Text] [Related]
8. VAN: an R package for identifying biologically perturbed networks via differential variability analysis. Jayaswal V, Schramm SJ, Mann GJ, Wilkins MR, Yang YH. BMC Res Notes; 2013 Oct 25; 6():430. PubMed ID: 24156242 [Abstract] [Full Text] [Related]
12. Predicting the interactome of Xanthomonas oryzae pathovar oryzae for target selection and DB service. Kim JG, Park D, Kim BC, Cho SW, Kim YT, Park YJ, Cho HJ, Park H, Kim KB, Yoon KO, Park SJ, Lee BM, Bhak J. BMC Bioinformatics; 2008 Jan 24; 9():41. PubMed ID: 18215330 [Abstract] [Full Text] [Related]