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Sökning: WFRF:(Rocke David M)

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1.
  • Clermont, Gilles, et al. (författare)
  • Bridging the gap between systems biology and medicine
  • 2009
  • Ingår i: Genome Medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 1:9
  • Tidskriftsartikel (refereegranskat)abstract
    • ABSTRACT : Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.
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2.
  • Halloran, John T., et al. (författare)
  • Speeding Up Percolator
  • 2019
  • Ingår i: Journal of Proteome Research. - : AMER CHEMICAL SOC. - 1535-3893 .- 1535-3907. ; 18:9, s. 3353-3359
  • Tidskriftsartikel (refereegranskat)abstract
    • The processing of peptide tandem mass spectrometry data involves matching observed spectra against a sequence database. The ranking and calibration of these peptide-spectrum matches can be improved substantially using a machine learning postprocessor. Here, we describe our efforts to speed up one widely used postprocessor, Percolator. The improved software is dramatically faster than the previous version of Percolator, even when using relatively few processors. We tested the new version of Percolator on a data set containing over 215 million spectra and recorded an overall reduction to 23% of the running time as compared to the unoptimized code. We also show that the memory footprint required by these speedups is modest relative to that of the original version of Percolator.
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