We address the visual comparison of multiple phylogenetic trees that arises in evolutionary biology, specifically between one reference tree and a collection of dozens to hundreds of other trees. We abstract the domain questions of phylogenetic tree comparison as tasks to look for supporting or conflicting evidence for hypotheses that requires inspection of both topological structure and attribute values at different levels of detail in the tree collection. We introduce the new visual encoding idiom of aggregated dendrograms to concisely summarize the topological relationships between interactively chosen focal subtrees according to biologically meaningful criteria, and provide a layout algorithm that automatically adapts to the available screen space. We design and implement the ADView system, which represents trees at multiple levels of detail across multiple views: the entire collection, a subset of trees, an individual tree, specific subtrees of interest, and the individual branch level. We benchmark the algorithms developed for ADView, compare its information density to previous work, and demonstrate its utility for quickly gathering evidence about biological hypotheses through usage scenarios with data from recently published phylogenetic analysis and case studies of expert use with real-world data, drawn from a summative interview study.
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http://dx.doi.org/10.1109/TVCG.2019.2898186 | DOI Listing |
Anal Chim Acta
April 2024
State Key Laboratory of Natural Medicines, Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, School of Basic Medical Sciences and Clinical Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu, 211198, China. Electronic address:
Herbicides have been extensively used around the world, which poses a potential hazard to humans and wildlife. Accurate detection of herbicides is crucial for the environment and human health. Herein, a simple and sensitive fluorescence sensor array was constructed for discrimination and identification of herbicides.
View Article and Find Full Text PDFScand J Rheumatol
September 2024
The Parker Institute, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Copenhagen, Denmark.
Objective: This study aimed to explore whether phenotypic characteristics of patients with chronic widespread pain (CWP) and fibromyalgia (FM) can be aggregated into definable clusters that may help to tailor treatments.
Method: Baseline variables (sex, age, education, marital/employment status, pain duration, prior CWP/FM diagnosis, concomitant rheumatic disease, analgesics, tender point count, and disease variables derived from standardized questionnaires) collected from 1099 patients (93.4% females, mean age 44.
PLoS One
July 2023
Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, VA, United States of America.
Background: There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality.
Objective: The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency.
Methods: Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%).
Pak J Biol Sci
January 2022
<b>Background and Objective:</b> Water quality in freshwater bodies is involved with multiple aspects such as physical, chemical and biological processes and their interactions. Due to the vulnerability of water resources, quality control of surface water is one of the key issues in environmental conservation programs. The objectives of the present work were to study the water quality in the Anzali Lagoon and to study the classification of water based on the water quality index in different parts of the Anzali Lagoon.
View Article and Find Full Text PDFGenes (Basel)
June 2022
Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
In recent years microbiome studies have become increasingly prevalent and large-scale. Through high-throughput sequencing technologies and well-established analytical pipelines, relative abundance data of operational taxonomic units and their associated taxonomic structures are routinely produced. Since such data can be extremely sparse and high dimensional, there is often a genuine need for dimension reduction to facilitate data visualization and downstream statistical analysis.
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