{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ochoteco Asensio J"],"funding":["Seventh Framework Programme"],"pagination":["2057-2069"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9077535"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20"],"pubmed_abstract":["Proteins are often considered the main biological element in charge of the different functions and structures of a cell. However, proteomics, the global study of all expressed proteins, often performed by mass spectrometry, is limited by its stochastic sampling and can only quantify a limited amount of protein per sample. Transcriptomics, which allows an exhaustive analysis of all expressed transcripts, is often used as a surrogate. However, the transcript level does not present a high level of correlation with the corresponding protein level, notably due to the existence of several post-transcriptional regulatory mechanisms. In this publication, we hypothesize that the missing protein values in proteomics could be predicted using machine learning regression methods, trained with many feat"],"journal":["Computational and structural biotechnology journal"],"pubmed_title":["Predicting missing proteomics values using machine learning: Filling the gap using transcriptomics and other biological features."],"pmcid":["PMC9077535"],"funding_grant_id":["602156"],"pubmed_authors":["Caiment F","Ochoteco Asensio J","Verheijen M"],"additional_accession":[]},"is_claimable":false,"name":"Predicting missing proteomics values using machine learning: Filling the gap using transcriptomics and other biological features.","description":"Proteins are often considered the main biological element in charge of the different functions and structures of a cell. However, proteomics, the global study of all expressed proteins, often performed by mass spectrometry, is limited by its stochastic sampling and can only quantify a limited amount of protein per sample. Transcriptomics, which allows an exhaustive analysis of all expressed transcripts, is often used as a surrogate. However, the transcript level does not present a high level of correlation with the corresponding protein level, notably due to the existence of several post-transcriptional regulatory mechanisms. In this publication, we hypothesize that the missing protein values in proteomics could be predicted using machine learning regression methods, trained with many feat","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2026-05-31T20:59:43.847Z","creation":"2025-02-19T04:21:03.794Z"},"accession":"S-EPMC9077535","cross_references":{"pubmed":["35601960"],"doi":["10.1016/j.csbj.2022.04.017"]}}