The document presents a seminar on multi-view design patterns and responsive visualization for genomics data, detailing its importance in clinical trials and the challenges in understanding genome patterns. It highlights the use of gosling grammar to enhance genome data visualization and includes a literature survey of existing methodologies in responsive genomics visualizations. The document also outlines the objectives of pre-processing data, enhancing models, and identifying multi-patterns in genome tracks.

1. Multi-View Design Patterns and
Responsive Visualization for
Genomics Data
Seminar On
Under the Guidance : Submitted By:
Dr. S. B. Gurav Vidya Vijay Mali

2. Agenda
 Abstract
 Introduction
 Motivation
 Objective
 Literature survey
 Overview Diagram
 References
2

3. Abstract
 Genome data is very crucial in clinical trails
 Understanding the genome patterns are always a
challenging task
 Using multi pattern analysis always enhances the model
 Using of Gosling grammar enhances the process of
genome data Visualization
3

4. 4
Introduction
 Genome data
 Genome patterns
 Gosling Grammar
 Genome data visualization

5. Motivation
 Handling huge genome data is always a tedious
work
 Identifying the pattern according to the need is
a challenging task in genome data
5

6. Objectives
 To preprocess the data properly
 To enhance the model with Gosling grammar
 To identify the multi-pattern in genome tracks
6

7. LITERATURE SURVEY
Paper Author Methodology
Multi-View Design
Patterns and Responsive
Visualization for
Genomics Data
Sehi L’Yi and Nils
Gehlenborg
In this paper, Author’s provide a reusable and
generalizable system for designing responsive
genomics data multi-view visualizations in this
research. Reviewing web-based genomics
visualization tools in the wild helps us understand
design difficulties. Using a taxonomy of
responsive designs, Author’s discover that tools
rarely promote responsiveness. Author’s identify
typical view composition patterns such “vertically
long,” “horizontally wide,” “circular,” and “cross-
shaped” to organize survey findings. Then they
identify their usability difficulties at multiple
resolutions based on composition patterns and
discuss ways to fix them and make genomics
visualizations responsive.
7

8. LITERATURE SURVEY
Paper Author Methodology
Adaptively
Exploring
Population Mobility
Patterns in Flow
Visualization
Fei Wang, Wei Chen, Ye
Zhao, Tianyu Gu, Siyuan
Gao, and Hujun Bao
This paper presents a system that deciphers,
transforms, searches, and visualizes records from
millions of city users. Author’s created MobiHash, a
data structure that receives phone call records from
base stations and indexes them using a Voronoi
partition of metropolitan space. MobiHash enables
interactive retrieval of population flow trajectories in
areas of interest using responsive data searches. To
avoid visual clutter and occlusions, population
movement is depicted as vector fields. A unique
radiation model interpolates population passing
zones due to sparse moving points. Author’s
validated the usability and efficiency of our
approach by analyzing population movement trends
over time using case studies and expert feedback.
8

9. LITERATURE SURVEY
Paper Author Methodology
Active Brainwave Pattern
Generation for Brain-To-
Machine Communication
Swathi Ganesh, Dale
Timm, Kee S. Moon,
Sung Q Lee, and Woosub
Youm
This paper aims to develop a real-time
EEG-based brain-to-machine
communication system by generating
distinct signals and identifying their
patterns for self-induced visual and
auditory stimuli. The brain-to-machine
communication system captures, analyzes,
and visualizes brain signal patterns in real-
time for medical applications like
rehabilitation, robotic control, and smart
wheelchairs.
9

10. Overview diagram
10

11. References
 S. L'Yi and N. Gehlenborg, "Multi-View Design Patterns and Responsive
Visualization for Genomics Data," in IEEE Transactions on Visualization
and Computer Graphics, vol. 29, no. 1, pp. 559-569, Jan. 2023, doi:
10.1109/TVCG.2022.3209398.
 F. Wang, W. Chen, Y. Zhao, T. Gu, S. Gao and H. Bao, "Adaptively
Exploring Population Mobility Patterns in Flow Visualization," in IEEE
Transactions on Intelligent Transportation Systems, vol. 18, no. 8, pp.
2250-2259, Aug. 2017, doi: 10.1109/TITS.2017.2711644.
 S. Ganesh, D. Timm, K. S. Moon, S. Q. Lee and W. Youm, "Active
brainwave pattern generation for brain-to-machine communication," 2017
39th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC), Jeju, Korea (South), 2017, pp.
990-993, doi: 10.1109/EMBC.2017.8036992.
11