Paper Info
Title
Visual analytics for supporting evidence-based interpretation of molecular cytogenomic findings
Abstract
Interpreting molecular cytogenomic findings that cover the human genome (e.g., microarray results) is challenging, as it requires accessing and working with multiple, diverse sources of data that are often large and heterogeneous. These data need to be accessed, queried, and simultaneously integrated to achieve open-ended goals, such as interpreting findings to make diagnoses and engage in genetic counselling. Currently, typical workflows of users are laborious, as data sources are often not integrated and must be accessed separately. Furthermore, large document sets often have to be combed through to assist in interpretation. Analytics tools are needed to help users process and distill large bodies of information into manageable sizes so the most relevant portions can be focused on. Current tools typically do not offer support for interactively exploring and engaging with visual representations of important entities and relationships (e.g., chromosomes, gene-phenotype relationships, and scientific articles). We present VErdICT, a visual analytics tool that can support users in their interpretation of molecular cytogenomic findings. A participatory design approach was taken to make VErdICT human-centered. We describe its development, usability and usefulness, and outline some future research challenges.
Year
DOI
Venue
2015
10.1145/2836034.2836036
VAHC
Field
DocType
Citations 
Data science,Participatory design,Interaction design,Computer science,Usability,Visual analytics,Analytics,Workflow,User-centered design,Evidence-based practice
Conference
1
PageRank 
References 
Authors
0.41
21
6
Name
Order
Citations
PageRank
Paul Parsons1367.31
Kamran Sedig211715.66
Robert E. Mercer325446.93
Maryam Khordad4101.64
Joan Knoll510.41
Peter K Rogan6403.14