Interpreting protein abundance in Saccharomyces cerevisiae through relational learning
Artikel i vetenskaplig tidskrift, 2024

Motivation: Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies. Results: By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as a-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances. Availability and implementation: All data and processing scripts are available at the following Github repository: https://github.com/ DanielBrunnsaker/ProtPredict.

Författare

Daniel Brunnsåker

Chalmers, Data- och informationsteknik, Data Science och AI

Filip Kronström

Chalmers, Data- och informationsteknik, Data Science och AI

Ievgeniia Tiukova

Chalmers, Life sciences, Infrastrukturer

Kungliga Tekniska Högskolan (KTH)

Ross King

Alan Turing Institute

Chalmers, Data- och informationsteknik, Data Science och AI

University of Cambridge

Bioinformatics

1367-4803 (ISSN) 13674811 (eISSN)

Vol. 40 2 btae050

Ämneskategorier

Biokemi och molekylärbiologi

Mikrobiologi

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1093/bioinformatics/btae050

PubMed

38273672

Mer information

Senast uppdaterat

2024-03-08