A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection.

ABSTRACT: The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein's sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our

SUBMITTER: Crook OM 

PROVIDER: S-EPMC7707549 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature