title: Inference of gene regulatory networks using time-series data: a survey.
abstract: The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository.
authors.affiliation: Computational Biology Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.
authors.forename: Chao
authors.initials: C
authors.lastname: Sima
authors.forename: Jianping
authors.initials: J
authors.lastname: Hua
authors.forename: Sungwon
authors.initials: S
authors.lastname: Jung