Bródka P. Chmiel A. Magnani M. Ragozini G. (2018). Quantifying layer similarity in multiplex networks: a systematic study. R. Soc. Open Sci. 5, 171747. 10.1098/rsos.171747
Buphamalai P. Kokotovic T. Nagy V. Menche J. (2021). Network analysis reveals rare disease signatures across multiple levels of biological organization. Nat. Commun. 12, 6306. 10.1038/s41467-021-26674-1
Corsello S. M. Nagari R. T. Spangler R. D. Rossen J. Kocak M. Bryan J. G. et al. (2020). Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer 1, 235–248. 10.1038/s43018-019-0018-6
de la Fuente A. (2010). From ‘differential expression’ to ‘differential networking’ - identification of dysfunctional regulatory networks in diseases. Trends Genet. 26, 326–333. 10.1016/j.tig.2010.05.001
Fuller T. F. Ghazalpour A. Aten J. E. Drake T. A. Lusis A. J. Horvath S. (2007). Weighted gene coexpression network analysis strategies applied to mouse weight. Mamm. Genome 18, 463–472. 10.1007/s00335-007-9043-3
Giri A. K. et al. (2023). Exome-wide association study reveals 7 functional variants associated with ex-vivo drug response in acute myeloid leukaemia patients. bioRxiv. 10.1101/2023.08.02.23290523
Hamilton W. L. et al. (2017). Representation learning on graphs: methods and applications. arXiv [cs.SI]. 10.48550/arXiv.1709.05584
Hammoud Z. Kramer F. (2020). Multilayer networks: aspects, implementations, and application in biomedicine. Big Data Anal. 5, 2. 10.1186/s41044-020-00046-0
Howell A. Howell S. J. (2023). Tamoxifen evolution. Br. J. Cancer 128, 421–425. 10.1038/s41416-023-02158-5
Ietswaart R. Gyori B. M. Bachman J. A. Sorger P. K. Churchman L. S. (2021). GeneWalk identifies relevant gene functions for a biological context using network representation learning. Genome Biol. 22, 55. 10.1186/s13059-021-02264-8
Kuijjer M. L. Hsieh P. H. Quackenbush J. Glass K. (2019b). lionessR: single sample network inference in R. BMC Cancer 19, 1003. 10.1186/s12885-019-6235-7
Kuijjer M. L. Tung M. G. Yuan G. Quackenbush J. Glass K. (2019a). Estimating sample-specific regulatory networks. iScience 14, 226–240. 10.1016/j.isci.2019.03.021
Lichtblau Y. Zimmermann K. Haldemann B. Lenze D. Hummel M. Leser U. (2017). Comparative assessment of differential network analysis methods. Brief. Bioinform. 18, 837–850. 10.1093/bib/bbw061
Liu H. Liu K. Bodenner D. L. (2005). Estrogen receptor inhibits interleukin-6 gene expression by disruption of nuclear factor kappaB transactivation. Cytokine 31, 251–257. 10.1016/j.cyto.2004.12.008
Loscalzo J. (2017) Network medicine harvard university press.
Mahapatra S. Bhuyan R. Das J. Swarnkar T. (2021). Integrated multiplex network based approach for hub gene identification in oral cancer. Heliyon 7, e07418. 10.1016/j.heliyon.2021.e07418
Matariek G. Teibo J. O. Elsamman K. Teibo T. K. A. Olatunji D. I. Matareek A. et al. (2022). Tamoxifen: the past, present, and future of a previous orphan drug. EJMED 4, 1–10. 10.24018/ejmed.2022.4.3.1124
Melograna F. Li Z. Galazzo G. van Best N. Mommers M. Penders J. et al. (2023). Edge and modular significance assessment in individual-specific networks. Sci. Rep. 13, 7868. 10.1038/s41598-023-34759-8
Peng J. Zhou Y. Wang K. (2021). Multiplex gene and phenotype network to characterize shared genetic pathways of epilepsy and autism. Sci. Rep. 11, 952. 10.1038/s41598-020-78654-y
Piasecka B. Duffy D. Urrutia A. Quach H. Patin E. Posseme C. et al. (2018). Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges. Proc. Natl. Acad. Sci. U. S. A. 115, E488–E497. 10.1073/pnas.1714765115
Shao N. Lu Z. Zhang Y. Wang M. Li W. Hu Z. et al. (2015). Interleukin-8 upregulates integrin β3 expression and promotes estrogen receptor-negative breast cancer cell invasion by activating the PI3K/Akt/NF-κB pathway. Cancer Lett. 364, 165–172. 10.1016/j.canlet.2015.05.009
Thomas S. Rouilly V. Patin E. Alanio C. Dubois A. Delval C. et al. (2015). The Milieu Intérieur study—an integrative approach for study of human immunological variance. Clin. Immunol. 157, 277–293. 10.1016/j.clim.2014.12.004
Van Dam S. Võsa U. van der Graaf A. Franke L. de Magalhães J. P. (2018). Gene co-expression analysis for functional classification and gene-disease predictions. Brief. Bioinform. 19, 575–592. 10.1093/bib/bbw139
Yi L. Pimentel H. Bray N. L. Pachter L. (2018). Gene-level differential analysis at transcript-level resolution. Genome Biol. 19, 53. 10.1186/s13059-018-1419-z
Yousefi B. Melograna F. Galazzo G. van Best N. Mommers M. Penders J. et al. (2023). Capturing the dynamics of microbial interactions through individual-specific networks. Front. Microbiol. 14, 1170391. 10.3389/fmicb.2023.1170391
Zheng D. Liwinski T. Elinav E. (2020). Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506. 10.1038/s41422-020-0332-7