Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature 2016;537: 347-55.
Hartwell LH, Hopfield JJ, Leibler S, et al. From molecular to modular cell biology. Nature 1999;402:C47-52.
Hewick RM, Lu Z, WangJH. Proteomics in drug discovery. Adv Protein Chem 2003;65:309-42.
Aranda S, Mas G, Di Croce L. Regulation of gene transcription by Polycomb proteins. Sci Adv 2015;1:e1500737.
Simonis N, van Helden J, Cohen GN, et al. Transcriptional regulation of protein complexes in yeast. Genome Biol 2004; 5:R33.
Tan K, Shlomi T, Feizi H, et al. Transcriptional regulation of protein complexes within and across species. Proc Natl Acad Sci USA 2007;104:1283-8.
Webb EC, Westhead DR. The transcriptional regulation of protein complexes; a cross-species perspective. Genomics 2009;94:369-76.
Becher I, Andres-Pons A, Romanov N, et al. Pervasive protein thermal stability variation during the cell cycle. Cell 2018;173:1495-1507.e18.
Chen Y, Chen S, Li K, et al. Overdosage of balanced protein complexes reduces proliferation rate in Aneuploid cells.Cell Syst 2019;9:129-142.e5.
D'Avino PP, Archambault V, Przewloka MR, et al. Isolation of protein complexes involved in mitosis and cytokinesis from Drosophila cultured cells. Melhods Mol Biol 2009;545: 99-112.
Pawson T, Nash P. Protein-protein interactions define specificity in signal transduction. Genes Dev 2000;14:1027-47.
Rebois RV, Hebert TE. Protein complexes involved in heptahe-lical receptor-mediated signal transduction. Receptors Channels 2003;9:169-94.
Chitwood PJ, Hegde RS. An intramembrane chaperone complex facilitates membrane protein biogenesis. Nature 2020;584: 630-4.
Sahu I, Mali SM, Sulkshane P, et al. The 20S as a stand-alone proteasome in cells can degrade the ubiquitin tag. Nat Commun 2021;12:6173.
Bludau I, Aebersold R. Proteomic and interactomic insights into the molecular basis of cell functional diversity. Nat Rev Mol Cell Biol 2020;21:327-40.
Skinnider MA, Foster LJ. Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments. Nat Methods 2021;18:806-15.
Fossati A, Li C, Uliana F, et al. PCprophet: a framework for protein complex prediction and differential analysis using proteomic data. Nat Methods 2021;18:520-7.
Hu LZ, Goebels F, Tan JH, et al. EPIC: software toolkit for elu-tion profile-based inference of protein complexes. Nat Methods 2019;16:737-42.
Stacey RG, Skinnider MA, Scott NE, et al. A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE). BMC Bioinform 2017;18:457.
Heusel M, Bludau I, Rosenberger G, et al. Complex-centric proteome profiling by SEC-SWATH-MS. Mol Syst Biol 2019;15: e8438.
Heusel M, Frank M, Kohler M, et al. A global screen for assembly state changes of the mitotic proteome by SEC-SWATH-MS. Cell Syst 2020;10:133-155.e6.
Gingras AC, Gstaiger M, Raught B, et al. Analysis of protein complexes using mass spectrometry. Nat Rev Mol Cell Biol 2007;8: 645-54.
Mehnert M, Ciuffa R, Frommelt F, et al. Multi-layered proteomic analyses decode compositional and functional effects of cancer mutations on kinase complexes. Nat Commun 2020;11:3563.
Liu F, Rijkers DT, Post H, et al. Proteome-wide profiling of protein assemblies by cross-linking mass spectrometry. Nat Methods 2015;12:1179-84.
Yu C, Huang L. Cross-linking mass spectrometry: an emerging Technology for Interactomics and Structural Biology.Anal Chem 2018;90:144-65.
Mateus A, Kurzawa N, Becher I, et al. Thermal proteome profiling for interrogating protein interactions. Mol Syst Biol 2020;16:e9232.
Schopper S, Kahraman A, Leuenberger P, et al. Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry. Nat Protoc 2017;12:2391-410.
Stoeger T, Gerlach M, Morimoto RI, et al. Large-scale investigation of the reasons why potentially important genes are ignored. PLoS Biol 2018;16:e2006643.
Edwards AM, Isserlin R, Bader GD, et al. Too many roads not taken. Nature 2011;470:163-5.
Islamaj Dogan R, Kim S, Chatr-Aryamontri A, et al. Overview of the BioCreative VI precision medicine track: mining protein interactions and mutations for precision medicine. Database (Oxford) 2019;2019:bay147. https://doi.org/10.1093/database/ bay147.
Li M, He Q, Ma J, et al. PPICurator: a tool for extracting comprehensive protein-protein interaction information. Proteomics 2019;19:e1800291.
Subramani S, Kalpana R, Monickaraj PM, et al. HPIminer: a text mining system for building and visualizing human protein interaction networks and pathways. J Biomed Inform 2015;54: 121-31.
Yu K, Lung PY, Zhao T, et al. Automatic extraction of protein-protein interactions using grammatical relationship graph.BMC Med Inform Decis Mak2018;18:42.
Manica M, Mathis R, Cadow J, et al. Context-specific interaction networks from vector representation of words. Nat Mach Intell 2019;1:181-90.
Gaizauskas R, Demetriou G, Artymiuk PJ, et al. Protein structures and information extraction from biological texts: the PASTA system.Bioinformatics 2003;19:135-43.
Cejuela JM, Vinchurkar S, Goldberg T, et al. LocText: relation extraction of protein localizations to assist database curation. BMC Bioinform 2018;19:15.
Pletscher-Frankild S, Palleja A, Tsafou K, et al. DISEASES: text miningand data integration ofdisease-gene associations.Meth-ods 2015;74:83-9.
Giurgiu M, Reinhard J, Brauner B, et al. CORUM: the compre-hensiveresourceofmammalianproteincomplexes-2019.Nucleic Acids Res 2019;47:D559-63.
Meldal BHM, Bye AJH, Gajdos L, et al. Complex portal 2018: extended content and enhanced visualization tools for macro-molecular complexes.Nucleic Acids Res 2019;47:D550-8.
Orchard S, Ammari M, Aranda B, et al. The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases. NucleAc Acids Res 2014;42:D358-63.
Wang JZ, Du Z, Payattakool R, et al. A new method to measure the semantic similarity of GO terms. Bioinformatics 2007;23: 1274-81.
Binns D, Dimmer E, Huntley R, et al. QuickGO: a web-based tool for gene ontology searching. Bioinformatics 2009;25:3045-6.
Joulin A, Grave E, Bojanowski P et al. Bag of tricks for efficient text classification. In: The 15th Conference of the European Chapter of the Association for Computational Linguistics. Valencia, Spain, 2017, p. 5. Association for Computational Linguistics.
Freidman JH, Bentley JL, Finkel RA. An algorithm for finding best matches in logarithmic expected time. ACM Trans Math Softw 1977;3:209.
Breiman L. Random forests. Mach Learn 2001;45:5-32.
Lecessie S, Vanhouwelingen JC. Ridge estimators in logistic-regression. Appl Stat 1992;41:191-201.
Zhang H. The optimality of Naive Bayes. In: Valerie Barr and Zdravko Markov (eds.) The Seventeenth International Florida Artificial Intelligence Research Society Conference. Menlo Park, California: AAAI, 2004, 562-67.
Massague J, Seoane J, Wotton D. Smad transcription factors. Genes Dev 2005;19:2783-810.
Wicks SJ, Lui S, Abdel-Wahab N, et al. Inactivation of smad-transforming growth factor beta signaling by Ca(2+)-calmodulin-dependent protein kinase II. Mol Cell Biol 2000;20:8103-11.
Liberati NT, Datto MB, Frederick JP, el al. Smads bind directly to the Jun family of AP-1 transcription factors. Proc Natl Acad Sci U SA 1999;96:4844-9.
Barutcuoglu Z, Schapire RE, Troyanskaya OG. Hierarchical multi-label prediction of gene function. Bioinformatics 2006;22: 830-6.
Guan Y, Myers CL, Hess DC, et al. Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biol 2008;9 (Suppl 1):S3.
Stojanova D, Ceci M, Malerba D, et al. Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction. BMC Bioinform 2013;14:285.
NakabayashiY, Kawashima S, EnomotoT, et al. Roles of common subunits within distinct multisubunit complexes. Proc Natl Acad SciUSA 2014;111:699-704.
Matalon O, Horovitz A, Levy ED. Different subunits belonging to the same protein complex often exhibit discordant expression levels and evolutionary properties. Curr Opin Struct Biol 2014; 26:113-20.
Jeffery CJ. Why study moonlighting proteins Front Genet 2015;6:211.
Li F, DongS, Leier A, et al. Positive-unlabeled learning in bioinfor-matics and computational biology: a brief review. Brief Bioinform 2022;23:bbab461. https://doi.org/10.1093/bib/bbab461.
Li F, Zhang Y, Purcell AW, et al. Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinform 2019;20:112.
Li XL, Liu B. Learning from positive and unlabeled examples with different data distributions, In: Joao Gama, Rui Camacho, Pavel B. Brazdil, Alipio Mario Jorge, Luis Torgo (eds.) 16th European Conference on Machine Learning, 2005 (ECMU05), Porto, Portugal, Germany: Springer, 2005, 3720:218-29.
Li X, Wu M, Kwoh CK, et al. Computational approaches for detecting protein complexes from protein interaction networks: a survey. BMC Genomics 2010;11 Suppl 1:S3.
QiY, Balem F, Faloutsos C, et al. Protein complex identification by supervised graph local clustering. Bioinformatics 2008;24:i250-8.
Himmelstein DS, Lizee A, Hessler C, el al. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. Elife 2017;6:e26726. https://doi.org/10.7554/eLife.26726.
Gligorijevic V, Renfrew PD, Kosciolek T, et al. Structure-based protein function prediction using graph convolutional networks. Nat Commun 2021;12:3168.
Long Y,Wu M,Kwoh CK, et al. Predicting human microbe-drug associations via graph convolutional network with conditional random field. Bioinformatics 2020;36:4918-27.
Wang K, Wang J, Domeniconi C, et al. Differentiating isoform functions with collaborative matrix factorization. Bioinformatics 2020;36:1864-71.
Zhang ZC, Zhang XF, Wu M, et al. A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks. Bioinformatics 2020;36: 3474-81.
Rosenberger G, Heusel M, Bludau I, et al. SECAT: quantifying protein complex dynamics across cell states by network-centric analysis of SEC-SWATH-MS profiles. Cell Syst 2020;11: 589-607.e8.
