SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis.

In oncology, Deep Learning has shown great potential to personalise tasks such as tumour type classification, based on per-patient omics data-sets. Being high dimensional, incorporation of such data in one model is a challenge, often leading to one-dimensional studies and, therefore, information loss. Instead, we first propose relying on non-fixed sets of whole genome or whole exome variant-associated sequences, which can be used for supervised learning of oncology-relevant tasks by our Set Transformer based Deep Neural Network, SetQuence. We optimise this architecture to improve its efficiency. This allows for exploration of not just coding but also non-coding variants, from large datasets. Second, we extend the model to incorporate these representations together with multiple other sources of omics data in a flexible way with SetOmic. Evaluation, using these representations, shows improved robustness and reduced information loss compared to previous approaches, while still being computationally tractable. By means of Explainable Artificial Intelligence methods, our models are able to recapitulate the biological contribution of highly attributed features in the tumours studied. This validation opens the door to novel directions in multi-faceted genome and exome wide biomarker discovery and personalised treatment among other presently clinically relevant tasks.

Workflow-enhanced conformational analysis of guanidine zinc complexes via a science gateway.

The new science gateway MoSGrid (Molecular Simulation Grid) enables users to submit and process molecular simulation studies on a large scale. A conformational analysis of guanidine zinc complexes, which are active catalysts in the ring-opening polymerization of lactide, is presented as an example. Such a large-scale quantum chemical study is enabled by workflow technologies. Two times 40 conformers have been generated, for two guanidine zinc complexes. Their structures were optimized using Gaussian03 and the energies processed within the quantum chemistry portlet of the MoSGrid portal. All meta- and post-processing steps have been performed in this portlet. All workflow features are implemented via WS-PGRADE and submitted to UNICORE.