The taxa R package provides a set of tools for defining and manipulating taxonomic data. The recent and widespread application of DNA sequencing to community composition studies is making large data sets with taxonomic information commonplace. However, compared to typical tabular data, this information is encoded in many different ways and the hierarchical nature of taxonomic classifications makes it difficult to work with. There are many R packages that use taxonomic data to varying degrees but there is currently no cross-package standard for how this information is encoded and manipulated. We developed the R package taxa to provide a robust and flexible solution to storing and manipulating taxonomic data in R and any application-specific information associated with it. Taxa provides parsers that can read common sources of taxonomic information (taxon IDs, sequence IDs, taxon names, and classifications) from nearly any format while preserving associated data. Once parsed, the taxonomic data and any associated data can be manipulated using a cohesive set of functions modeled after the popular R package dplyr. These functions take into account the hierarchical nature of taxa and can modify the taxonomy or associated data in such a way that both are kept in sync. Taxa is currently being used by the metacoder and taxize packages, which provide broadly useful functionality that we hope will speed adoption by users and developers.
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http://dx.doi.org/10.12688/f1000research.14013.2 | DOI Listing |
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 PDFSci 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 PDFArch 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 PDFPain 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 PDFSci 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.
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