Welcome to the ISMB 2020 virtual platform. The International Society for Computational Biology (ISCB) is grateful for your support and participation as we navigate these unprecedented times. We, like you, were devastated when we had to cancel our face to face conference, but we knew that we needed to find a way to continue to advance computational research and provide our members with a platform to disseminate research findings and network.
Over the course of the next four days, you will have the opportunity to hear and interact live with speakers presenting over 400 talks in 22 Community of Special Interest (COSI) tracks, Special Sessions, Technology Tracks, and a Workshop on Bioinformatics Education (WEB). You will also be able to browse more than 700 posters in the virtual Poster Hall, where authors will be standing by to answer your questions in the chat feature. And don’t forget to stop by our sponsorship and exhibitor section to learn more about publishing opportunities, services, tools, and job openings.
Biological networks have the power to map cellular function, but only when unified to overcome their individual limitations such as bias and noise. Unsupervised network integration addresses this, automatically weighting input information to obtain an accurate, unified result. However, existing unsupervised network integration methods do not adequately scale to the number of nodes and networks present in genome-scale data and do not handle frequently encountered data characteristics (e.g. partial network overlap). To address this, we have developed an unsupervised deep learning-based network integration algorithm that incorporates recent advances in reasoning over unstructured data – namely the Graph Convolutional Network (GCN) – that can effectively learn dependencies between physical, co-expression and genetic interaction network topologies. Our method, BIONIC (Biological Network Integration using Convolutions), produces high quality gene and protein features which capture and unify information across many diverse functional interaction networks. BIONIC learns features which contain substantially more functional information compared to existing approaches, linking genes and proteins that share co-complex, pathway and bioprocess relationships.
Join us for Panel Presentations and a Discussion session in the CAMDA Cafe room by clicking the CAMDA logo under "Cafe Connect". We will discuss latest insights and shape future Camda Contest Challenges!