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Interpreting protein abundance in Saccharomyces cerevisiae through relational learning

Brunnsåker, Daniel, 1992 (author)
Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden; Department of Computer Science and Engineering, Chalmers University of Technology, Rännvägen 6B, Gothenburg 412 96, Sweden., Rännvägen 6B
Kronström, Filip, 1995 (author)
Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
Tiukova, Ievgeniia, 1987 (author)
KTH,Industriell bioteknologi,Department of Life Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden,Chalmers tekniska högskola,Chalmers University of Technology,Kungliga Tekniska Högskolan (KTH),Royal Institute of Technology (KTH)
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King, Ross, 1962 (author)
The Alan Turing Institute,Chalmers tekniska högskola,Chalmers University of Technology,University Of Cambridge,Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom; The Alan Turing Institute, London NW1 2DB, United Kingdom
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Chalmers tekniska högskola Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden; Department of Computer Science and Engineering, Chalmers University of Technology, Rännvägen 6B, Gothenburg 412 96, Sweden, Rännvägen 6B (creator_code:org_t)
Oxford University Press, 2024
2024
English.
In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 40:2
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
NATURVETENSKAP  -- Biologi -- Mikrobiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Microbiology (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

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