Dr. Adriano Barbosa-Silva has received his PhD in Bioinformatics by the Federal University of Minas Gerais (Brazil) after training at the Structural and Computational Biology Unit of the European Molecular Biology Laboratory in Heidelberg (Germany). After he was a post-doc at the Max-Delbrueck Center for Molecular Medicine in Berlin (Germany) at the Computational Biology and Data Mining Group. Since 2013 Dr. Barbosa-Silva is a Research Associate of the Bioinformatics Core at the Luxembourg Centre for Molecular Medicine at the University of Luxembourg. Dr. Barbosa-Silva is member of the International Society for Computational Biology (ICSB) and contributes as scientific collaborator of the EU/EFPIA IMI projects eTRIKS and AETIONOMY. Furthermore Dr. Barbosa-Silva is a cofounder of the company Information Technology for Translational Medicine (ITTM S.A.) silver member of the tranSMART Foundation.
Session A2: tranSMART Platform Release 16.2
Title: An open-source platform for interactive visual analytics for translational research data
In translational research efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical, or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing community consisting of academia and pharma. However, tranSMART currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason we developed SmartR, a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements.
Title: Linking tranSMART data to brain imaging exploration and visualization tools via a dedicated API.
Brain imaging analysis has proven to be a powerful approach to discover biomarkers that could be used to stratify or predict neurodegenerative diseases progression on affected patients. We developed a dedicated API in order to fetch tranSMART data collected from brain diffusion-weighted magnetic resonance imaging (DTI) and cortex parcellation techniques that generates volumetric and connectomics data. Further we integrated this information along with data visualization software in order to better analyze the information stored on tranSMART.