Latent network-based representations for large-scale gene expression data analysis.

ABSTRACT: BACKGROUND:With the recent advancements in high-throughput experimental procedures, biologists are gathering huge quantities of data. A main priority in bioinformatics and computational biology is to provide system level analytical tools capable of meeting an ever-growing production of high-throughput biological data while taking into account its biological context. In gene expression data analysis, genes have widely been considered as independent components. However, a systemic view shows that they act synergistically in living cells, forming functional complexes and more generally a biological system. RESULTS:In this paper, we propose LATNET, a signal transformation framework that, starting from an initial large-scale gene expression data, allows to generate new representations based on

SUBMITTER: Dhifli W 

PROVIDER: S-EPMC7394327 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature