References
1. Bader, G. D., Cary, M. P. & Sander, C. Pathguide: a Pathway Resource List. Nucleic Acids Res. 34 , D504–D506 (2006).
2. Sorger, P. K. A reductionist’s systems biology: opinion. Curr. Opin. Cell Biol. 17 , 9–11 (2005).
3. Weinberg, R. Point: Hypotheses first. Nature 464 , 678–678 (2010).
4. Golub, T. Counterpoint: Data first. Nature 464 , 679–679 (2010).
5. Fabregat, A. et al. The Reactome Pathway Knowledgebase.Nucleic Acids Res. 46 , D649–D655 (2018).
6. Lo Surdo, P. et al. SIGNOR 3.0, the SIGnaling network open resource 3.0: 2022 update. Nucleic Acids Res. 51 , D631–D637 (2023).
7. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs.Nucleic Acids Res. 45 , D353–D361 (2017).
8. Swainston, N. et al. Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12 , 109 (2016).
9. Ostaszewski, M. et al. COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms. Mol. Syst. Biol. 17 , e10387 (2021).
10. Valenzuela-Escárcega, M. A. et al. Large-scale automated machine reading discovers new cancer-driving mechanisms. Database2018 , bay098 (2018).
11. Conceição, S. I. R. & Couto, F. M. Text Mining for Building Biomedical Networks Using Cancer as a Case Study. Biomolecules11 , 1430 (2021).
12. Korkut, A. et al. Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells. eLife 4 , e04640 (2015).
13. Messina, F. et al. COVID-19: viral–host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection. J. Transl. Med. 18 , 233 (2020).
14. Meysman, P. et al. Protein complex analysis: From raw protein lists to protein interaction networks. Mass Spectrom. Rev.36 , 600–614 (2017).
15. Gordon, D. E. et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583 , 459–468 (2020).
16. Yamada, R., Okada, D., Wang, J., Basak, T. & Koyama, S. Interpretation of omics data analyses. J. Hum. Genet.66 , 93–102 (2021).
17. Tolani, P., Gupta, S., Yadav, K., Aggarwal, S. & Yadav, A. K. Chapter Four - Big data, integrative omics and network biology. inAdvances in Protein Chemistry and Structural Biology (eds. Donev, R. & Karabencheva-Christova, T.) vol. 127 127–160 (Academic Press, 2021).
18. Scannell, J. W. & Bosley, J. When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis. PLOS ONE11 , e0147215 (2016).
19. Muldoon, J. J., Yu, J. S., Fassia, M.-K. & Bagheri, N. Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants. Bioinformatics35 , 3421–3432 (2019).
20. Mubeen, S. et al. The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling. Front. Genet. 10 , (2019).
21. Garrido-Rodriguez, M., Zirngibl, K., Ivanova, O., Lobentanzer, S. & Saez-Rodriguez, J. Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks. Mol. Syst. Biol. 18 , e11036 (2022).
22. Silverman, E. K. et al. Molecular Networks in Network Medicine: Development and Applications. Wiley Interdiscip. Rev. Syst. Biol. Med. 12 , e1489 (2020).
23. Ranea, J. A. G., Perkins, J., Chagoyen, M., Díaz-Santiago, E. & Pazos, F. Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View. Genes 13 , 1081 (2022).
24. Smith, B. & Varzi, A. C. Fiat and Bona Fide Boundaries.Philos. Phenomenol. Res. 60 , 401–420 (2000).
25. Karp, P. D. et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief. Bioinform. 20 , 1085–1093 (2019).
26. Altman, T., Travers, M., Kothari, A., Caspi, R. & Karp, P. D. A systematic comparison of the MetaCyc and KEGG pathway databases.BMC Bioinformatics 14 , 112 (2013).
27. Hornbeck, P. V. et al. PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse.Nucleic Acids Res. 40 , D261-270 (2012).
28. Babur, Ö. et al. Causal interactions from proteomic profiles: Molecular data meet pathway knowledge. Patterns 2 , 100257 (2021).
29. Kohn, K. W., Aladjem, M. I., Kim, S., Weinstein, J. N. & Pommier, Y. Depicting combinatorial complexity with the molecular interaction map notation. Mol. Syst. Biol. 2 , 51 (2006).
30. Chang, A. et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res.49 , D498–D508 (2021).
31. Griss, J. et al. ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis. Mol. Cell. Proteomics 19 , 2115–2125 (2020).
32. Tarca, A. L., Draghici, S., Bhatti, G. & Romero, R. Down-weighting overlapping genes improves gene set analysis. BMC Bioinformatics13 , 136 (2012).
33. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43 , e47 (2015).
34. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics14 , 7 (2013).
35. Bowers, R. R. et al. SWAN pathway-network identification of common aneuploidy-based oncogenic drivers. Nucleic Acids Res.50 , 3673–3692 (2022).
36. Chereda, H. et al. Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer. Genome Med. 13 , 42 (2021).
37. Cerami, E. G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39 , D685–D690 (2011).
38. Ayati, M., Chance, M. R. & Koyutürk, M. Co-phosphorylation networks reveal subtype-specific signaling modules in breast cancer.Bioinforma. Oxf. Engl. 37 , 221–228 (2021).
39. Pačínková, A. & Popovici, V. Using empirical biological knowledge to infer regulatory networks from multi-omics data. BMC Bioinformatics 23 , 351 (2022).
40. Werhli, A. V. & Husmeier, D. Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Mol. Biol. 6 , Article15 (2007).
41. Alghamdi, N. et al. A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data. Genome Res. 31 , 1867–1884 (2021).
42. Pratapa, A., Balachandran, S. & Raman, K. Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks.Bioinformatics 31 , 3299–3305 (2015).
43. Di Filippo, M. et al. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLOS Comput. Biol. 18 , e1009337 (2022).
44. Huang, L., Currais, A. & Shokhirev, M. N. SUMMER, a shiny utility for metabolomics and multiomics exploratory research. Metabolomics Off. J. Metabolomic Soc. 16 , 126 (2020).
45. Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution. Cell 181 , 236–249 (2020).
46. Luo, R. et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform.23 , bbac409 (2022).
47. Wong, J. V. et al. Author-sourced capture of pathway knowledge in computable form using Biofactoid. eLife 10 , e68292.
48. Todorov, P. V., Gyori, B. M., Bachman, J. A. & Sorger, P. K. INDRA-IPM: interactive pathway modeling using natural language with automated assembly. Bioinforma. Oxf. Engl. 35 , 4501–4503 (2019).