Semi-supervised prediction of gene regulatory networks using machine learning algorithms

@article{Patel2015SemisupervisedPO,
  title={Semi-supervised prediction of gene regulatory networks using machine learning algorithms},
  author={Nihir Patel and Jason Tsong-Li Wang},
  journal={Journal of Biosciences},
  year={2015},
  volume={40},
  pages={731-740}
}
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised… CONTINUE READING
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