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Efficient explorati...
Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
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- Kling, Teresia, 1985 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Sahlgrenska Cancer Center,Institute of Medicine, Department of Molecular and Clinical Medicine,University of Gothenburg,Univ Gothenburg, Sahlgrenska Canc Ctr, SE-40530 Gothenburg, Sweden.;Univ Gothenburg, Dept Mol & Clin Med, SE-40530 Gothenburg, Sweden.
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- Johansson, Patrik (författare)
- Uppsala universitet,Neuroonkologi,Science for Life Laboratory, SciLifeLab
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- Sánchez, José, 1979 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper, matematisk statistik,Department of Mathematical Sciences, Mathematical Statistics,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology,Univ Gothenburg, Math Sci, SE-41296 Gothenburg, Sweden.;Chalmers, SE-41296 Gothenburg, Sweden.
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- Marinescu, Voichita D. (författare)
- Uppsala universitet,Institutionen för medicinsk biokemi och mikrobiologi,Science for Life Laboratory, SciLifeLab
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- Jörnsten, Rebecka, 1971 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper, matematisk statistik,Department of Mathematical Sciences, Mathematical Statistics,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology,Univ Gothenburg, Math Sci, SE-41296 Gothenburg, Sweden.;Chalmers, SE-41296 Gothenburg, Sweden.
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- Nelander, Sven (författare)
- Uppsala universitet,Neuroonkologi,Science for Life Laboratory, SciLifeLab
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(creator_code:org_t)
- 2015-05-07
- 2015
- Engelska.
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 43:15
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Abstract
Ämnesord
Stäng
- Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets. © 2015 The Author(s).
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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