TY  - JOUR
A1  - Razaghi-Moghadam, Zahra
A1  - Nikoloski, Zoran
T1  - Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data
T2  - npj Systems biology and applications
N2  - Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data. By employing the data fromEscherichia coliandSaccharomyces cerevisiaeas well as synthetic networks from the DREAM4 and five network inference challenges, we demonstrate that our GRADIS approach outperforms the state-of-the-art supervised and unsupervided approaches. This holds when predictions about target genes for individual transcription factors as well as for the entire network are considered. We employ experimentally verified GRNs fromE. coliandS. cerevisiaeto validate the predictions and obtain further insights in the performance of the proposed approach. Our GRADIS approach offers the possibility for usage of other network-based representations of large-scale data, and can be readily extended to help the characterisation of other cellular networks, including protein-protein and protein-metabolite interactions.
Y1  - 2020
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/58070
SN  - 2056-7189
VL  - 6
IS  - 1
PB  - Nature Publ. Group
CY  - London
ER  -