There is an explosion of knowledge in medical science in the form of peer-reviewed publications, abstracts, case presentations, white papers, grant submissions, editorials, specialty-society guidelines, cases reported to medical registries and patient generated feedback on the quality of healthcare. Integrating these vast quantities of data, synthesizing it effectively and intelligently, and displaying that information in an intuitive format can facilitate improved human decision-making both in clinical practice and medical research. The development of a knowledge visualization tool for advancing translational science requires information visualization of massive unstructured data, and of multi-dimension, multi-hierarchy and highly connected structured data. Furthermore, it is also challenging to present the data visually with the limitations of computational power and human cognitive capability. To address these challenges, IAI proposes to develop SMS-VAT: a Scalable Multi-Scale Visual Analytical Tool for Advancing Translational Sciences. The key innovation of SMS-VAT is to integrate OnLine Analytical Processing (OLAP) like data cubing, multi-dimensional and network based visual analytics, and data mining algorithms to significantly reduce the complexity of visualizing translational science knowledge from massive unstructured data. With the multi-dimension and multi-hierarchy data modeled in data cubes, their multi-scale paradigms and the underlying summarization algorithms will help users to easily navigate to the right dimension and hierarchy for visualization. To control the number of dimensions and data points in the visualization, different clustering algorithms will be developed to reduce computational complexity and also human-cognition complexity. SMS-VAT consists of three important components of heterogeneous information extraction and mining for translational science, integration of cubing architecture with powerful visualization, and enhanced multi-scale visual analytics.