Evidence map›Paper›PMID 36191105›Full record

ArticleIEEE transactions on visualization and computer graphics2023

GenoREC: A Recommendation System for Interactive Genomics Data Visualization.

Aditeya Pandey, Sehi L'Yi, Qianwen Wang, Michelle A Borkin, Nils Gehlenborg

Abstract read
In one paragraph

Article in IEEE transactions on visualization and computer graphics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Aardvark: Composite Visualizations of Trees, Time-Series, and Images.IEEE transactions on visualization and computer graphics · 2025
    Article
  2. Learnable and Expressive Visualization Authoring Through Blended Interfaces.IEEE transactions on visualization and computer graphics · 2025
    Article
  3. Article
  4. Article
  5. Multi-View Design Patterns and Responsive Visualization for Genomics Data.IEEE transactions on visualization and computer graphics · 2023
    Review
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Aditeya Pandey
Sehi L'Yi
Qianwen Wang
Michelle A Borkin
Nils Gehlenborg

Funding

4D Nucleome Network Data Coordination and Integration CenterU01CA200059 · NCI · HARVARD MEDICAL SCHOOL · PI PARK, PETER J · 2015 to 2024
$24.4M
Center for 3D Structure and Physics of the GenomeUM1HG011536 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI DEKKER, JOB, MIRNY, LEONID A · 2020 to 2024
$11.8M
Developing Informatics Technologies to Model Cancer Gene RegulationU24CA237617 · NCI · DANA-FARBER CANCER INST · PI MEYER, CLIFFORD · 2019 to 2023
$3.6M
Grammar-Driven Genomic Data VisualizationR01HG011773 · NHGRI · HARVARD MEDICAL SCHOOL · PI GEHLENBORG, NILS · 2022 to 2025
$2.3M
NCI NIH HHS U01 CA200059NCI NIH HHS U24 CA237617NHGRI NIH HHS R01 HG011773NHGRI NIH HHS UM1 HG011536
6 · The paper itself

Abstract

Interpretation of genomics data is critically reliant on the application of a wide range of visualization tools. A large number of visualization techniques for genomics data and different analysis tasks pose a significant challenge for analysts: which visualization technique is most likely to help them generate insights into their data? Since genomics analysts typically have limited training in data visualization, their choices are often based on trial and error or guided by technical details, such as data formats that a specific tool can load. This approach prevents them from making effective visualization choices for the many combinations of data types and analysis questions they encounter in their work. Visualization recommendation systems assist non-experts in creating data visualization by recommending appropriate visualizations based on the data and task characteristics. However, existing visualization recommendation systems are not designed to handle domain-specific problems. To address these challenges, we designed GenoREC, a novel visualization recommendation system for genomics. GenoREC enables genomics analysts to select effective visualizations based on a description of their data and analysis tasks. Here, we present the recommendation model that uses a knowledge-based method for choosing appropriate visualizations and a web application that enables analysts to input their requirements, explore recommended visualizations, and export them for their usage. Furthermore, we present the results of two user studies demonstrating that GenoREC recommends visualizations that are both accepted by domain experts and suited to address the given genomics analysis problem. All supplemental materials are available at https://osf.io/y73pt/.

Indexed as

Computer GraphicsData VisualizationGenomicsSoftware

Identifiers

PMID36191105
PMCPMC10067538

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.