Portrait of Georgy Derevyanko is unavailable

Georgy Derevyanko

Alumni

Publications

Deep convolutional networks for quality assessment of protein folds
Georgy Derevyanko
Sergei Grudinin
Yoshua Bengio
Guillaume Lamoureux
Motivation The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of prote… (see more)in models. Most assessment methods rank candidate models using heavily engineered structural features, defined as complex functions of the atomic coordinates. However, very few methods have attempted to learn these features directly from the data. Results We show that deep convolutional networks can be used to predict the ranking of model structures solely on the basis of their raw three-dimensional atomic densities, without any feature tuning. We develop a deep neural network that performs on par with state-of-the-art algorithms from the literature. The network is trained on decoys from the CASP7 to CASP10 datasets and its performance is tested on the CASP11 dataset. Additional testing on decoys from the CASP12, CAMEO and 3DRobot datasets confirms that the network performs consistently well across a variety of protein structures. While the network learns to assess structural decoys globally and does not rely on any predefined features, it can be analyzed to show that it implicitly identifies regions that deviate from the native structure. Availability and implementation The code and the datasets are available at https://github.com/lamoureux-lab/3DCNN_MQA. Supplementary information Supplementary data are available at Bioinformatics online.
2018-06-19
Bioinformatics (published)
doi.org
arxiv.org
Towards Gene Expression Convolutions using Gene Interaction Graphs
Francis Dutil
Joseph Paul Cohen
Martin Weiss
Georgy Derevyanko
Yoshua Bengio
We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the… (see more) data, however is it not discovered automatically given the noise and low numbers of samples used in most research. We discuss how gene interaction graphs (same pathway, protein-protein, co-expression, or research paper text association) can be used to impose a bias on a deep model similar to the spatial bias imposed by convolutions on an image. We explore the usage of Graph Convolutional Neural Networks coupled with dropout and gene embeddings to utilize the graph information. We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used. We conclude that more work should be done in this direction. We design experiments that show why existing methods fail to capture signal that is present in the data when features are added which clearly isolates the problem that needs to be addressed.
2018-06-18
ArXiv (preprint)
arxiv.org
arxiv.org
Graph Priors for Deep Neural Networks
Francis Dutil
Joseph Paul Cohen
Martin Weiss
Georgy Derevyanko
Yoshua Bengio
In this work we explore how gene-gene interaction graphs can be used as a prior for the representation of a model to construct features base… (see more)d on known interactions between genes. Most existing machine learning work on graphs focuses on building models when data is confined to a graph structure. In this work we focus on using the information from a graph to build better representations in our models. We use the percolate task, determining if a path exists across a grid for a set of node values, as a proxy for gene pathways. We create variants of the percolate task to explore where existing methods fail. We test the limits of existing methods in order to determine what can be improved when applying these methods to a real task. This leads us to propose new methods based on Graph Convolutional Networks (GCN) that use pooling and dropout to deal with noise in the graph prior.
2018-02-12
(published)
openreview.net
openreview.net