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Genome Biology

, 17:214

First Online: 19 October 2016Received: 13 June 2016Accepted: 03 October 2016

Abstract

We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.

KeywordsGene regulatory networks Inference Time series Electronic supplementary materialThe online version of this article doi:10.1186-s13059-016-1076-z contains supplementary material, which is available to authorized users.

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Autor: Kevin A. McGoff - Xin Guo - Anastasia Deckard - Christina M. Kelliher - Adam R. Leman - Lauren J. Francey - John B. Ho

Fuente: https://link.springer.com/







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