Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks.

Genomics experts rely on visualization to extract and share insights from complex and large-scale datasets. Beyond off-the-shelf tools for data exploration, there is an increasing need for platforms that aid experts in authoring customized visualizations for both exploration and communication of insights. A variety of interactive techniques have been proposed for authoring data visualizations, such as template editing, shelf configuration, natural language input, and code editors.

Learnable and Expressive Visualization Authoring through Blended Interfaces.

A wide range of visualization authoring interfaces enable the creation of highly customized visualizations. However, prioritizing expressiveness often impedes the learnability of the authoring interface. The diversity of users, such as varying computational skills and prior experiences in user interfaces, makes it even more challenging for a single authoring interface to satisfy the needs of a broad audience.

PanVA: Pangenomic Variant Analysis.

Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data.

Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression.

Prcis: We updated a clinical decision support tool integrating predicted visual field (VF) metrics from an artificial intelligence model and assessed clinician perceptions of the predicted VF metric in this usability study.

Purpose: To evaluate clinician perceptions of a prototyped clinical decision support (CDS) tool that integrates visual field (VF) metric predictions from artificial intelligence (AI) models.

Methods: Ten ophthalmologists and optometrists from the University of California San Diego participated in 6 cases from 6 patients, consisting of 11 eyes, uploaded to a CDS tool ("GLANCE", designed to help clinicians "at a glance").