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DNetPRO: A network approach for low-dimensional signatures from high-throughput data

View ORCID ProfileNico Curti, Enrico Giampieri, Giuseppe Levi, Gastone Castellani, Daniel Remondini
doi: https://doi.org/10.1101/773622
Nico Curti
1Department of Physics and Astronomy, University of Bologna
3INFN Bologna
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  • ORCID record for Nico Curti
  • For correspondence: nico.curti2@unibo.it
Enrico Giampieri
2Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna
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Giuseppe Levi
1Department of Physics and Astronomy, University of Bologna
3INFN Bologna
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Gastone Castellani
1Department of Physics and Astronomy, University of Bologna
3INFN Bologna
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Daniel Remondini
1Department of Physics and Astronomy, University of Bologna
3INFN Bologna
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Abstract

The objective of many high-throughput “omics” studies is to obtain a relatively low-dimensional set of observables - signature - for sample classification purposes (diagnosis, prognosis, stratification). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised signature identification method based on a bottom-up combinatorial approach that exploits the discriminant power of all variable pairs. The algorithm is easily scalable allowing efficient computing even for high number of observables (104 − 105). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or compares to them but with a smaller number of selected variables. Moreover the linearity of DNetPRO allows a clearer interpretation of the obtained signatures in comparison to non linear classification models

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  • https://github.com/Nico-Curti/DNetPRO

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Posted September 19, 2019.
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DNetPRO: A network approach for low-dimensional signatures from high-throughput data
Nico Curti, Enrico Giampieri, Giuseppe Levi, Gastone Castellani, Daniel Remondini
bioRxiv 773622; doi: https://doi.org/10.1101/773622
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DNetPRO: A network approach for low-dimensional signatures from high-throughput data
Nico Curti, Enrico Giampieri, Giuseppe Levi, Gastone Castellani, Daniel Remondini
bioRxiv 773622; doi: https://doi.org/10.1101/773622

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