A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Statistical and machine learning methods to study human CD4+ T cell proteome profiles
Tekijät: Suomi Tomi, Elo Laura L
Kustantaja: Elsevier
Julkaisuvuosi: 2022
Journal: Immunology Letters
Tietokannassa oleva lehden nimi: Immunology letters
Lehden akronyymi: Immunol Lett
Vuosikerta: 245
Aloitussivu: 8
Lopetussivu: 17
ISSN: 0165-2478
eISSN: 1879-0542
DOI: https://doi.org/10.1016/j.imlet.2022.03.006
Verkko-osoite: https://doi.org/10.1016/j.imlet.2022.03.006
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/175189283
Mass spectrometry proteomics has become an important part of modern immunology, making major contributions to understanding protein expression levels, subcellular localizations, posttranslational modifications, and interactions in various immune cell populations. New developments in both experimental and computational techniques offer increasing opportunities for exploring the immune system and the molecular mechanisms involved in immune responses. Here, we focus on current computational approaches to infer relevant information from large mass spectrometry based protein profiling datasets, covering the different steps of the analysis from protein identification and quantification to further mining and modelling of the protein abundance data. Additionally, we provide a summary of the key proteome profiling studies on human CD4+ T cells and their different subtypes in health and disease.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
