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Uncovering cancer g...
Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data
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- Seçilmiş, Deniz (författare)
- Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Box 1031, S-17121 Solna, Sweden.
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- Hillerton, Thomas (författare)
- Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Box 1031, S-17121 Solna, Sweden.
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- Morgan, Daniel (författare)
- Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Box 1031, S-17121 Solna, Sweden.
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- Tjärnberg, Andreas (författare)
- NYU, Ctr Dev Genet, New York, NY USA.
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- Nelander, Sven (författare)
- Uppsala universitet,Science for Life Laboratory, SciLifeLab,Neuroonkologi
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- Nordling, Torbjörn E. M. (författare)
- Natl Cheng Kung Univ, Dept Mech Engn, Tainan, Taiwan.
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- Sonnhammer, Erik L. L. (författare)
- Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Box 1031, S-17121 Solna, Sweden.
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(creator_code:org_t)
- 2020-11-09
- 2020
- Engelska.
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Ingår i: npj Systems Biology and Applications. - : Springer Science and Business Media LLC. - 2056-7189. ; 6:1
- Relaterad länk:
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https://doi.org/10.1...
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https://www.nature.c...
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https://uu.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where similar to 1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.
Ämnesord
- NATURVETENSKAP -- Biologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences (hsv//eng)
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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