PT  - JOURNAL ARTICLE
AU  - Curti, Nico
AU  - Giampieri, Enrico
AU  - Levi, Giuseppe
AU  - Castellani, Gastone
AU  - Remondini, Daniel
TI  - DNetPRO: A network approach for low-dimensional signatures from high-throughput data
AID  - 10.1101/773622
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 773622
4099  - http://biorxiv.org/content/early/2019/09/19/773622.short
4100  - http://biorxiv.org/content/early/2019/09/19/773622.full
AB  - 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