@inproceedings{PB-VRVis-2019-027,
author = {Alemzadeh, Shiva and Kromp, Florian and Preim, Bernhard and Taschner-Mandl, Sabine and B{\"u}hler, Katja},
title = {A Visual Analytics Approach for Patient Stratification and
  Biomarker Discovery},
year = {2019},
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Kozl{\'{\i}}kov{\'a}, Barbora and Linsen, Lars and V{\'a}zquez, Pere-Pau and Lawonn, Kai
  and Raidou, Renata Georgia},
doi = {10.2312/vcbm.20191235},
url = {https://www.vrvis.at/publications/PB-VRVis-2019-027},
publisher = {The Eurographics Association},
isbn = {978-3-03868-081-9},
abstract = {We introduce discoVA as a visual analytics tool for the  refinement of risk stratification of cancer patients and  biomarker discovery.  Currently, tools for the joint analysis of  multiple biological and clinical information in this field are  insufficient or lacking. Our tool fills this gap by enabling  bio-medical experts to explore datasets of cancer patient  cohorts.  By using multiple coordinated visualization  techniques, nested visual queries on various data types can be  performed to generate/prove a hypothesis by identifying discrete  sub-cohorts. We demonstrated the utility of discoVA by a case  study involving bio-medical researchers.},
}