| 1. |
- Swat, M. J., et al.
(author)
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Pharmacometrics Markup Language (PharmML) : Opening New Perspectives for Model Exchange in Drug Development
- 2015
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In: CPT. - : American Society for Clinical Pharmacology & Therapeutics. - 2163-8306. ; 4:6, s. 316-319
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Journal article (peer-reviewed)abstract
- The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.
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| 2. |
- Thiele, I., et al.
(author)
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A community-driven global reconstruction of human metabolism
- 2013
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In: Nature Biotechnology. - : Springer Science and Business Media LLC. - 1087-0156 .- 1546-1696. ; 31:5, s. 419-
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Journal article (peer-reviewed)abstract
- Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including similar to 2x more reactions and similar to 1.7x more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
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| 3. |
- Deans, Andrew R, et al.
(author)
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Finding Our Way through Phenotypes.
- 2015
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In: PLoS Biology. - : Public Library of Science (PLoS). - 1545-7885. ; 13:1
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Journal article (peer-reviewed)abstract
- Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.
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