Predicting missing proteomics values using machine learning: Filling the gap using transcriptomics and other biological features.

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

SUBMITTER: Ochoteco Asensio J 

PROVIDER: S-EPMC9077535 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature