information visualization tools
DESCRIPTION
Information Visualization Tools. Ketan Mane Ph.D. Candidate Member of Information Visualization Lab Member of Cyberinfrastructure for Network Science School of Library and Information Science (SLIS) Indiana University, Bloomington, IN [email protected]. This Presentation has Three Parts. - PowerPoint PPT PresentationTRANSCRIPT
Information Visualization Tools
Ketan ManePh.D. CandidateMember of Information Visualization LabMember of Cyberinfrastructure for Network ScienceSchool of Library and Information Science (SLIS)Indiana University, Bloomington, [email protected]
This Presentation has Three Parts
1. Information Retrieval Systems
2. Knowledge Management Visualizations
3. Visual Computational Diagnostics of Acute Lymphoblastic Leukemia Patients
This Presentation has Three Parts
1. Information Retrieval Systems
2. Knowledge Management Visualizations
3. Visual Computational Diagnostics of Acute Lymphoblastic Leukemia Patients
Mane, Ketan & Börner, Katy. (2006). SRS Browser: A Visual Interface to Sequence Retrieval System Visualization and Data Analysis, San Jose, CA, SPIE-IS&T, Jan 15-19, 2006.
SRS Browser – Extends SRS Browser Functionality
SRS System
SRS Browser
Features of SRS Browser
Filter Network to Show Immediate Neighbors
Features of SRS Browser
Oncosifter
Hierarchical Visualization Interface
Graphical visualization reveal structure in data.
Cancer categories represent hierarchical tree data structure.
Radial tree is used to display cancer categories.
Category classification of cancer is easily available.
Minimum interaction needed to get information.
Hierarchical Search Interface and Corresponding Results Page
Oncosifter
This Presentation has Three Parts
1. Information Retrieval Systems
2. Knowledge Management Visualizations
3. Visual Computational Diagnostics of Acute Lymphoblastic Leukemia Patients
Co-word space of the top 50 highly frequent and bursty words used in the top 10% most highly cited PNAS publications in 1982-2001.
Mane & Börner. (2004) PNAS, 101(Suppl. 1):5287-5290.
Mapping Topic Bursts
11
12
Börner & Penumarthy.(2005)
PNAS citations received by top U.S. institutions
Policy
Economics
Statistics
Math
CompSci
Physics
Biology
GeoScience
Microbiology
BioChem
Brain
PsychiatryEnvironment
Vision
Virology Infectious Diseases
Cancer
MRI
Bio-Materials
Law
Plant
Animal
Phys-Chem
Chemistry
Psychology
Education
Computer Tech
GI
Funding Patterns of the National Institutes of Health (NIH)
13
Science map applications: Identifying core competency
Policy
Economics
Statistics
Math
CompSci
Physics
Biology
GeoScience
Microbiology
BioChem
Brain
PsychiatryEnvironment
Vision
Virology Infectious Diseases
Cancer
MRI
Bio-Materials
Law
Plant
Animal
Phys-Chem
Chemistry
Psychology
Education
Computer Tech
Funding Patterns of the National Science Foundation (NSF)
Science map applications: Identifying core competency
14Kevin W. Boyack & Richard Klavans, unpublished work.
Boyack, Kevin W., Mane, Ketan and Börner, Katy. (2004). Mapping Medline Papers, Genes, and Proteins Related to Melanoma Research. IV2004 Conference, London, UK, pp. 965-971.
Top Researched Genes/Proteins in Melanoma Research
Association Maps
Gene-Paper Network Gene-Gene Network
Boyack, Kevin W., Mane, Ketan and Börner, Katy. (2004). Mapping Medline Papers, Genes, and Proteins Related to Melanoma Research. IV2004 Conference, London, UK, pp. 965-971.
This Presentation has Three Parts
1. Information Retrieval Systems
2. Knowledge Management Visualizations
3. Visual Computational Diagnostics of Acute Lymphoblastic Leukemia Patients
Computational Diagnostics
Visualization Goal: Identify factors that cause relapse in
patients
Relapse insight can be gained by –
Global overview of medical condition of all patients in the
dataset
Ability to identify worst medical condition in patients
Comparing patient medical condition at diagnostic
variable(s) level
Ability to identify and compare patient groups that share
similar medical
condition across multiple variablesDr. Susanane Raggs Dr. Katy Börner Ketan Mane Julie Haydon Jada Pane
Computational Diagnostics – Tool Requested by Client
Matrix visualization
Phenotype and prognosis
Parallel Coordinate Visualization
Coupled Windows
Computational Diagnostics – Interactive Visualization
System Architecture
Computational Diagnostics - Dataset Details
Diagnostic data variables from medical records for Acute Lymphoblastic Leukemia (ALL) patients are categorized into a. Outcome Patient Variables: relapse, relapse site, alive/death status, and LDKA.
b. Biology Patient Variables: immunophenotype, genetic condition, WBC, Hgb, platelets, and CNS. c. Host Patient Variables: diagnostic age (ageDx), gender, and race.
d. Treatment Patient Variables: BM 7 and BM 14.
e. Social Factors Patient Variables: MFI-class, education level, %single family members, and % family employment.
All data was provided by Dr. Susanne Raggs, Julie Haydon and Jada Pane.
Matrix Visualization – Phenotype View
Data is shown independent of other variables.
Color codes help to provide a quick insight into patient medical
condition.
Matrix Visualization – Prognosis View
Color codes indicate event free survival in percent (%EFS).
All variable values are dependent on other variable values.
Matrix Visualization – Combined View
Facilitates selection of phenotype/prognosis view for individual
diagnostic variables.
Parallel Coordinates Visualization
Uses one axis for each data variable.
For each patient, all data values on different parallel axis are connected.
All patient graphs are shown here.
Single or multiple patients can be selected and studied in detail.
Parallel Coordinates Visualization
Tool-tip display to show diagnostic values of selected
patient.
Parallel Coordinates Visualization – User Interactions
Display axes-labels to mark different regions/values along
axes
Numerical landmarks along axes showing values for
quantitative variables.
Category labels used along axes show values for nominal
variables.
Parallel Coordinates Visualization – User Interactions
Display zones to show severity values for different variables
Triangular zones indicate variables with quantitative values.
Rectangular zones are used for variables with nominal values.
Parallel Coordinates Visualization – User Interactions
Axis selection to study global variations in patient values
Single axis can be selected to study the trend in patient values.
Red-to-green gradient used to indicate values along the
selected axis.
[Red = High value, Green = Low value]
Parallel Coordinates Visualization
A subset of patents can be selected and examined as a
group.
Parallel Coordinates Visualization
Simultaneous display of patient groups to study differences.
Patient Group 1
Patient Group 2
Patient Group 1 & 2
Parallel Coordinates Visualization
Multiple Coordinated Views
Patient can be selected and color coded in matrix view.
Corresponding patient lines are highlighted in parallel
coordinate view.
Demo of Medical Diagnostics Project