Supervised, semi-supervised and unsupervised inference of gene regulatory networks

Supervised, semi-supervised and unsupervised inference of gene regulatory networks

Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression da...

Supervised inference of gene - regulatory networks Cuong C To and

Background: Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks. Results: The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression tim...

Supervised Inference of Gene Regulatory Networks by Linear Programming

The development of algorithms for reverse-engineering gene regulatory networks is boosted by microarray technologies, which enable the simultaneous measurement of all RNA transcripts in a cell. Meanwhile the curated repository of regulatory associations between transcription factors (TF) and target genes is available based on bibliographic references. In this paper we propose a novel method to ...

SIRENE: supervised inference of regulatory networks

MOTIVATION Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks is thus needed to understand the cell's working mechanism, and can for example, be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory net...

Semi-supervised methods for gene regulatory networks 9 Month Report