Workshop: Bioinformatics and computational modeling - Supervised machine learning approaches for gene regulatory network reconstruction

Summary

The workshop was an introduction of supervised machine learning approach for reconstruction of gene regulatory networks (GRNs) using large-scale transcriptomics data. In the first part of the event, there was an overview of supervised machine learning approaches, namely support vector machines and random forests. We summarised how these approaches are used in addressing the problem of GRN reconstruction when transcriptomics data are available. We also covered aspects related to assessing the performance of reconstructions. The second part of the workshop was dedicated to showcasing the applications of these approaches to reconstruct GRNs in model plants and crops, with emphasis on Arabidopsis thaliana and maize. We also presented packages and tools developed in house that can be used in addressing this problem in future applications.

Lecturers:

There is no charge for this workshop, which is being run as a part of the training activities of project RESIST.

Sign up for Workshop: Bioinformatics and computational modeling - Supervised machine learning approaches for gene regulatory network reconstruction here, by May 17, 2021