D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

摘要

Gene regulation plays an important role in gene transcription [ , ], gene differentiation [ ], cell fate decisions [ , ], complex diseases [ ]. To elucidate the structure of gene regulatory networks (GRNs) has been a central effort of the interdisciplinary field of systems biology. With the advent of high-throughput technologies such as microarrays and RNA sequencing, tremendous amounts of data have been generated, which makes it feasible to infer GRNs from exclusive expression data or multiple classes of data based on computational methods [ ]. However, inferring the GRN only from gene expression data remains a daunting task due to the small number of available measurements and the high dimensional, noisy data.