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Multi-omic integrat...
Multi-omic integration by machine learning (MIMaL)
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- Dickinson, Q. (författare)
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, USA; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, USA
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- Aufschnaiter, Andreas (författare)
- Stockholms universitet,Institutionen för biokemi och biofysik
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- Ott, Martin, 1974 (författare)
- Stockholms universitet,Gothenburg University,Göteborgs universitet,Institutionen för biomedicin, avdelningen för medicinsk kemi och cellbiologi,Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology,Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden,Institutionen för biokemi och biofysik
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- Meyer, J. G. (författare)
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, USA; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, USA
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- Kohler, Andreas, Dr. rer. nat. 1988- (författare)
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
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(creator_code:org_t)
- 2022-09-15
- 2022
- Engelska.
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 38:21, s. 4908-4918
- Relaterad länk:
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Motivation: Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow for measuring the abundances of RNA, proteins, lipids and metabolites. These highly complex datasets reflect the states of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through the integration of these data remains challenging. Results: Connections between molecules in different omic layers were discovered through a combination of machine learning and model interpretation. Discovered connections reflected protein control (ProC) over metabolites. Proteins discovered to control citrate were mapped onto known genetic and metabolic networks, revealing that these protein regulators are novel. Further, clustering the magnitudes of ProC over all metabolites enabled the prediction of five gene functions, each of which was validated experimentally. Two uncharacterized genes, YJR120W and YDL157C, were accurately predicted to modulate mitochondrial translation. Functions for three incompletely characterized genes were also predicted and validated, including SDH9, ISC1 and FMP52. A website enables results exploration and also MIMaL analysis of user-supplied multi-omic data.
Ämnesord
- NATURVETENSKAP -- Matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics (hsv//eng)
- NATURVETENSKAP -- Biologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences (hsv//eng)
Nyckelord
- phosphosphingolipid phospholipase-c
- yeast
- pathway
- identification
- expression
- cytoscape
- proteins
- Biochemistry & Molecular Biology
- Biotechnology & Applied Microbiology
- Computer Science
- Mathematical & Computational Biology
- Mathematics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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