{"entity": "researcher", "timestamp": "2026-11-12T14:48:43.271Z", "family": "Song", "given": "Jiangning", "initials": "J", "orcid": "0000-0001-8031-9086", "affiliations": ["Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.", "Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/b28057226c9f4bcfbce293cbc72b424b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/b28057226c9f4bcfbce293cbc72b424b"}}, "publications": [{"entity": "publication", "iuid": "a3dd5905000e4180af6bd96410e7c8a1", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/a3dd5905000e4180af6bd96410e7c8a1.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/a3dd5905000e4180af6bd96410e7c8a1"}}, "title": "PCfun: a hybrid computational framework for systematic characterization of protein complex function.", "authors": [{"family": "Sharma", "given": "Varun S", "initials": "VS"}, {"family": "Fossati", "given": "Andrea", "initials": "A"}, {"family": "Ciuffa", "given": "Rodolfo", "initials": "R"}, {"family": "Buljan", "given": "Marija", "initials": "M"}, {"family": "Williams", "given": "Evan G", "initials": "EG"}, {"family": "Chen", "given": "Zhen", "initials": "Z"}, {"family": "Shao", "given": "Wenguang", "initials": "W"}, {"family": "Pedrioli", "given": "Patrick G A", "initials": "PGA"}, {"family": "Purcell", "given": "Anthony W", "initials": "AW"}, {"family": "Mart\u00ednez", "given": "Mar\u00eda Rodr\u00edguez", "initials": "MR"}, {"family": "Song", "given": "Jiangning", "initials": "J", "orcid": "0000-0001-8031-9086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b28057226c9f4bcfbce293cbc72b424b.json"}}, {"family": "Manica", "given": "Matteo", "initials": "M", "orcid": "0000-0002-8872-0269", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bbfba9c722c345aba5115ccbb5554865.json"}}, {"family": "Aebersold", "given": "Ruedi", "initials": "R"}, {"family": "Li", "given": "Chen", "initials": "C", "orcid": "0000-0002-1847-754X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b92b18e879ae435c89fb1175be6fa190.json"}}], "type": "journal article", "published": "2022-07-18", "journal": {"title": "Brief. Bioinformatics", "issn": "1477-4054", "volume": "23", "issue": "4", "issn-l": "1467-5463"}, "abstract": "In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes-or macromolecular machines-are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.", "doi": "10.1093/bib/bbac239", "pmid": "35724564", "labels": {"Andrea Fossati": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC9310514"}, {"db": "pii", "key": "6611913"}], "notes": [], "created": "2026-11-06T08:03:40.269Z", "modified": "2026-11-06T08:03:40.496Z"}, {"entity": "publication", "iuid": "8ff89aef021448118103660e3a1e7ef7", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/8ff89aef021448118103660e3a1e7ef7.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/8ff89aef021448118103660e3a1e7ef7"}}, "title": "PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.", "authors": [{"family": "Fossati", "given": "Andrea", "initials": "A"}, {"family": "Li", "given": "Chen", "initials": "C", "orcid": "0000-0002-1847-754X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b92b18e879ae435c89fb1175be6fa190.json"}}, {"family": "Uliana", "given": "Federico", "initials": "F"}, {"family": "Wendt", "given": "Fabian", "initials": "F", "orcid": "0000-0002-2501-536X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f9f0e10e0ff84bfdba2da72790f12c32.json"}}, {"family": "Frommelt", "given": "Fabian", "initials": "F", "orcid": "0000-0003-3666-8005", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1e71a93da1624a17bcbace26ba50c5a5.json"}}, {"family": "Sykacek", "given": "Peter", "initials": "P", "orcid": "0000-0001-8800-8354", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0506bf1817c145229c29c2b217f36590.json"}}, {"family": "Heusel", "given": "Moritz", "initials": "M", "orcid": "0000-0002-8506-530X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1be58c525be846cdb4ec2c66f5c8e2e1.json"}}, {"family": "Hallal", "given": "Mahmoud", "initials": "M"}, {"family": "Bludau", "given": "Isabell", "initials": "I"}, {"family": "Capraz", "given": "T\u00fcmay", "initials": "T", "orcid": "0000-0002-2547-067X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/53479f66fc6340568a485c5c1b99b7a4.json"}}, {"family": "Xue", "given": "Peng", "initials": "P", "orcid": "0000-0001-6844-6066", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/43aed6c67356473a83d88ef08d6ba794.json"}}, {"family": "Song", "given": "Jiangning", "initials": "J", "orcid": "0000-0001-8031-9086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b28057226c9f4bcfbce293cbc72b424b.json"}}, {"family": "Wollscheid", "given": "Bernd", "initials": "B"}, {"family": "Purcell", "given": "Anthony W", "initials": "AW", "orcid": "0000-0003-0532-8331", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7323f8fcf6684490a1f0958c2ce4402a.json"}}, {"family": "Gstaiger", "given": "Matthias", "initials": "M", "orcid": "0000-0002-3245-3253", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8370f12358274bb2a5a4cd524ae4f465.json"}}, {"family": "Aebersold", "given": "Ruedi", "initials": "R", "orcid": "0000-0002-9576-3267", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4aa50db862e94831b8cac04c4426daf1.json"}}], "type": "journal article", "published": "2021-05-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "18", "issue": "5", "pages": "520-527", "issn-l": "1548-7091"}, "abstract": "Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.", "doi": "10.1038/s41592-021-01107-5", "pmid": "33859439", "labels": {"Andrea Fossati": null, "DDLS Fellow": null}, "xrefs": [{"db": "pii", "key": "10.1038/s41592-021-01107-5"}], "notes": [], "created": "2026-11-06T08:03:47.052Z", "modified": "2026-11-06T08:03:47.407Z"}]}