activity
20182025
most citedDeep convolutional networks for quality assessment of protein folds

116 citations · 146 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Lightweight Optimal-Transport Harmonization on Edge Devices

Maria Larchenko, Dmitry Guskov, Alexander Lobashev +1

Color harmonization adjusts the colors of an inserted object so that it perceptually matches the surrounding image, resulting in a seamless composite. The harmonization problem nat…

q-bio.BM2022

Dock2D: Synthetic data for the molecular recognition problem

Siddharth Bhadra-Lobo, Georgy Derevyanko, Guillaume Lamoureux

Predicting the physical interaction of proteins is a cornerstone problem in computational biology. New classes of learning-based algorithms are actively being developed, and are ty…

cs.LG2018★ 2 cited

TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure

Georgy Derevyanko, Guillaume Lamoureux

Predicting the structure of a protein from its sequence is a cornerstone task of molecular biology. Established methods in the field, such as homology modeling and fragment assembl…

q-bio.GN2018★ 28 cited

Towards Gene Expression Convolutions using Gene Interaction Graphs

Francis Dutil, Joseph Paul Cohen, Martin Weiss +2

We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the data, however is it not discovered auto…

q-bio.BM2018★ 116 cited

Deep convolutional networks for quality assessment of protein folds

Georgy Derevyanko, Sergei Grudinin, Yoshua Bengio +1

The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of protein models. Most assessment methods rank candidate m…