All species are hierarchically related to one another, and we use taxonomic names to label the nodes in this hierarchy. Taxonomic data is becoming increasingly available on the web, but scientists need a way to access it in a programmatic fashion that's easy and reproducible. We have developed taxize, an open-source software package (freely available from http://cran.r-project.org/web/packages/taxize/index.html) for the R language. taxize provides simple, programmatic access to taxonomic data for 13 data sources around the web. We discuss the need for a taxonomic toolbelt in R, and outline a suite of use cases for which taxize is ideally suited (including a full workflow as an appendix). The taxize package facilitates open and reproducible science by allowing taxonomic data collection to be done in the open-source R platform.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901538 | PMC |
| http://dx.doi.org/10.12688/f1000research.2-191.v2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Weighted Echo State Graph Neural Networks Based on Robust and Epitaxial Film Memristors.
Adv Sci (Weinh)
January 2025
College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.
Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption.
View Article and Find Full Text PDFCognitive load detection through EEG lead wise feature optimization and ensemble classification.
Sci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes.
View Article and Find Full Text PDFGrouping of chemicals for safety assessment: the importance of toxicokinetic properties of salicylate esters.
Arch Toxicol
January 2025
Cosmetics Europe, Brussels, Belgium.
Grouping of chemicals has been proposed as a strategy to speed up the screening and identification of potential substances of concern among the broad chemical universe under REACH. Such grouping is usually based on shared structural features and should only be used for the prioritization objectives. However, additional considerations (as well as structural similarity) are needed, e.
View Article and Find Full Text PDFExploring Chronic Pain in Hemodialysis Patients: An Observational Study Based on the New IASP Classification for ICD-11.
Pain Ther
January 2025
Department of Medicine, Nephrology Division, University of Verona, Verona, Italy.
Introduction: Pain is one of the most frequently reported symptoms in hemodialyzed (HD) patients, with prevalence rates between 33% and 82%. Risk factors for chronic pain in HD patients are older age, long-lasting dialysis history, several concomitant diseases, malnutrition, and others. However, chronic pain assessment in HD patients is rarely performed by specialists in pain medicine, with relevant consequences in terms of diagnostic and treatment accuracy.
View Article and Find Full Text PDFMachine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.
Sci Rep
January 2025
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active drugs. We initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" in the subsequent sections of the manuscript) and MACCS fingerprints (referred to as "fingerprints" in the subsequent sections of the manuscript), thus generating 18 different ML QSAR models. Using the chemical spaces defined by the modeling descriptors and fingerprints, the similarity and error-based RASAR descriptors were computed, and the most discriminating RASAR descriptors were used to develop another set of 18 different ML c-RASAR models.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!