Machine Learning on Large-Scale Proteomics Data Identifies Tissue and Cell-Type Specific Proteins.

ABSTRACT: Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyze the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows sl

SUBMITTER: Claeys T 

PROVIDER: S-EPMC10088018 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature