Daniel Morgan


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Arbetar vid Institutionen för biokemi och biofysik
Besöksadress Svante Arrhenius väg 16
Rum Tomtebodavägen 23
Postadress Institutionen för biokemi och biofysik 106 91 Stockholm


I urval från Stockholms universitets publikationsdatabas
  • 2017. Andreas Tjarnberg (et al.). Molecular Biosystems 13 (7), 1304-1312

    A key question in network inference, that has not been properly answered, is what accuracy can be expected for a given biological dataset and inference method. We present GeneSPIDER - a Matlab package for tuning, running, and evaluating inference algorithms that allows independent control of network and data properties to enable data-driven benchmarking. GeneSPIDER is uniquely suited to address this question by first extracting salient properties from the experimental data and then generating simulated networks and data that closely match these properties. It enables data-driven algorithm selection, estimation of inference accuracy from biological data, and a more multifaceted benchmarking. Included are generic pipelines for the design of perturbation experiments, bootstrapping, analysis of linear dependence, sample selection, scaling of SNR, and performance evaluation. With GeneSPIDER we aim to move the goal of network inference benchmarks from simple performance measurement to a deeper understanding of how the accuracy of an algorithm is determined by different combinations of network and data properties.

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Senast uppdaterad: 13 september 2018

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