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1,392 results for “Match”
Pharmacogenomics datasets for Ontology Matching
<p><strong>Pharmacogenomics datasets for Ontology Matching</strong></p> <p>Pharmacogenomics (or PGx for short) involves n-ary tuples representing so-called "pharmacogenomic relationships" and their components of three distinct types: drugs, genetic factors, and phenotypes. Tuples are reified as instances of the class ``pgxo:PharmacogenomicRelationship``. The goal of the matching task is to match these tuples (instance matching).</p> <p>Motivation: Pharmacogenomic tuples involve drugs, genetic factors, and phenotypes, and state that patients being treated by the specified drugs while having the specified genetic factors may experience the given phenotypes. Knowledge in pharmacogenomics is scattered across several resources, e.g., reference databases (<a href="https://www.pharmgkb.org/">PharmGKB</a>) or the biomedical literature. Hence, there is a need to build a consolidated view of the knowledge of this domain by aligning tuples from different sources. See [1] for a detailed motivation and [2] for a detailed task description.</p> <p><strong>Datasets</strong></p> <p>We provide different subsets of the alignments available in <a href="https://pgxlod.loria.fr">PGxLOD</a> that have been created with the matching rules described in [3].</p> <p><em>Task with 10 % of PGx relationships</em></p> <ul> <li>Alignments: 942 <ul> <li>relatedMatch alignments: 83</li> <li>sameAs alignments: 315</li> <li>closeMatch alignments: 46</li> <li>broadMatch alignments: 332</li> <li>narrowMatch alignments: 166</li> </ul> </li> <li>Entities to align in source: 2523</li> <li>Triples in source: 211770</li> <li>Entities to align in target: 2520</li> <li>Triples in target: 208051</li> </ul> <p><em>Task with 50 % of PGx relationships</em></p> <ul> <li>Alignments: 23938 <ul> <li>relatedMatch alignments: 1500</li> <li>sameAs alignments: 8636</li> <li>closeMatch alignments: 1128</li> <li>broadMatch alignments: 8047</li> <li>narrowMatch alignments: 4627</li> </ul> </li> <li>Entities to align in source: 12767</li> <li>Triples in source: 459483</li> <li>Entities to align in target: 12450</li> <li>Triples in target: 450364</li> </ul> <p><em>Task with 100 % of PGx relationships</em></p> <ul> <li>Alignments: 88569 <ul> <li>relatedMatch alignments: 5318</li> <li>sameAs alignments: 34510</li> <li>closeMatch alignments: 3978</li> <li>broadMatch alignments: 24097</li> <li>narrowMatch alignments: 20666</li> </ul> </li> <li>Entities to align in source: 25362</li> <li>Triples in source: 691151</li> <li>Entities to align in target: 25073</li> <li>Triples in target: 687052</li> </ul> <p><strong>References</strong></p> <ol> <li>Pierre Monnin, Joël Legrand, Graziella Husson, Patrice Ringot, Andon Tchechmedjiev, Clément Jonquet, Amedeo Napoli, Adrien Coulet: PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison. BMC Bioinformatics 20-S(4): 139:1-139:16 (2019) <a href="https://doi.org/10.1186/s12859-019-2693-9">[pdf]</a></li> <li>Pierre Monnin, Adrien Coulet: Matching pharmacogenomic knowledge: particularities, results, and perspectives. OM@ISWC 2022: 79-83 <a href="https://ceur-ws.org/Vol-3324/om2022_STpaper3.pdf">[pdf]</a></li> <li>Pierre Monnin, Miguel Couceiro, Amedeo Napoli, Adrien Coulet: Knowledge-Based Matching of n-ary Tuples. ICCS 2020: 48-56 <a href="https://inria.hal.science/hal-02493067/document">[pdf]</a></li> </ol>
Microbiota or Placebo After Antimicrobial Therapy for Recurrent C. Difficile at Home (MATCH)
ClinicalTrials.gov study NCT03005379. IPD Sharing: YES. Countries: 1. Publications: 2.
The Risk of Venous Thromboembolism in Systemic Inflammatory Disorders: a United Kingdom (UK) Matched Cohort Study
ClinicalTrials.gov study NCT03835780. IPD Sharing: YES. Countries: 1. Publications: 2.
Phase III Study of RAD001 Adjuvant Therapy in Poor Risk Patients With Diffuse Large B-Cell Lymphoma (DLBCL) of RAD001 Versus Matching Placebo After Patients Have Achieved Complete Response With First-
ClinicalTrials.gov study NCT00790036. IPD Sharing: UNDECIDED. Countries: 34. Publications: 1.
Cleaning stations in coral reefs – matching services to reef-dwelling and pelagic clients?
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Data from: The article Euclimatch: An R package for climate matching with Euclidean distance metrics
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Evolution of a mosquito’s hatching behavior to match its human-provided habitat
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Pan Arctic Species List (PASL) matching script
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Trait matching affects the probability of nectar robbing in plant-pollinator networks
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Data and code from: Prey detection by a stepwise visual template matching mechanism
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Data from: Global plant-frugivore trait matching is shaped by climate and biogeographic history
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Data from: Pre‐adaptation and adaptation shape trait‐environment matching in the Neotropics
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Climate matching models for Ceratapion basicorne (Coleoptera: Apionidae), a biocontrol agent of yellow starthistle
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Data from: Background matching can reduce responsiveness of jumping spiders to stimuli in motion
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[Dataset] ERRATUM: Repairing Broken Locators in Web Test Scripts using Tree Matching
<p>Dataset and evaluation for the paper "ERRATUM: Repairing Broken Locators in Web Test Scripts using Tree Matching".</p> <p>Two datasets were used:</p> <p>- The wayback dataset (wayback_dataset.json)</p> <p>- The mutation dataset (mutation_dataset.json)</p> <p>Each one was used to evaluate both state-of-the-art locator repair solution WATER and our novel tree matching-based approach.<br> The results of this evaluation presented in our paper are in the following files:</p> <p>- eval_mutation.json</p> <p>- eval_wayback.json</p>
Latitudinal pattern in community-wide herbivory does not match the pattern in herbivory averaged across common plant species
<p>1. The latitudinal herbivory hypothesis (LHH) predicts that plant losses to herbivores decrease from low to high latitudes. Although the LHH is a community-level hypothesis, it has been rarely tested with data on community-wide herbivory, i.e. the percentage of annual production of foliar biomass consumed by insects from all plant species at a given site. Therefore, we asked whether community-wide leaf herbivory follows the same latitudinal pattern as observed for an unweighted average of herbivory across common plant species.</p> <p>2. We selected ten study sites in boreal forests from 60°N to 69°N along 1000 km long latitudinal gradient in NW Russia. We measured relative foliar losses to insect herbivores in seven woody plant species (jointly comprising over 95% of the community-wide aboveground biomass) and estimated their current-year foliar biomass. We averaged leaf herbivory for all seven species and calculated community-wide leaf herbivory by weighting the relative foliar losses of each plant species against the contribution of that species to the annual foliar biomass production.</p> <p>3. Leaf herbivory was five-fold higher in deciduous species than in conifers. Latitudinal patterns in herbivory varied from a significant poleward decrease in all deciduous species to a significant poleward increase in Norway spruce. Herbivory values, averaged across seven plant species, decreased with latitude and followed the pattern observed in deciduous plants due to their higher foliar losses compared with conifers. By contrast, community-wide herbivory did not change with latitude. This discrepancy emerged because the proportion of deciduous plant foliage in the community increased with increasing latitude, and this increase counterbalanced the simultaneous poleward decrease in losses of these species to insects.</p> <p>4. Synthesis The herbivory measured by averaging relative losses of individual plant species and the community-wide herbivory are likely to show different latitudinal patterns in various plant communities. The contributions of plant species to the total foliar biomass production should be taken into account in studies of spatial patterns of herbivory which test community-level hypotheses. This approach may provide new insight into macroecological research on biotic interactions and improve our understanding of the role of insect herbivores in ecosystem-level processes.</p>
Supplementary material for 'A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching'
<p>Supplementary material for the https://github.com/Living-with-machines/LwM_SIGSPATIAL2020_ToponymMatching repository, containing the underlying code and materials for the paper 'A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching', accepted to SIGSPATIAL2020 as a poster paper.</p> <p> </p> <p>Coll Ardanuy, M., Hosseini, K., McDonough, K., Krause, A., van Strien, D. and Nanni, F. (2020): A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching, SIGSPATIAL: Poster Paper.</p>
Data from: Trait matching and phenological overlap increase the spatio-temporal stability and functionality of plant-pollinator interactions
<p>Morphology and phenology influence plant-pollinator network structure, but whether they generate more stable pairwise interactions with higher pollination success is unknown. Here we evaluate the importance of morphological trait matching, phenological overlap and specialisation for the spatio-temporal stability (measured as variability) of plant-pollinator interactions and for pollination success, while controlling for species abundance. To this end, we combined a six-year plant-pollinator interaction dataset, with information on species traits, phenologies, specialisation, abundance and pollination success, into structural equation models. Interactions among abundant plants and pollinators with well-matched traits and phenologies formed the stable and functional backbone of the pollination network, whereas poorly-matched interactions were variable in time and had lower pollination success. We conclude that phenological overlap could be more useful for predicting changes in species interactions than species abundances, and that non-random extinction of species with well-matched traits could decrease the stability of interactions within communities and reduce their functioning.</p>
Return to work following diagnosis of low-grade glioma: A nationwide matched cohort study
<p>Objective: Return-to-work (RTW) following diagnosis of infiltrative low-grade gliomas (LGG) is unknown. </p> <p>Methods: Swedish patients with histopathological verified WHO grade II diffuse glioma diagnosed between 2005-2015 were included. Data were acquired from several Swedish registries. A total of 381 patients aged 18-60 were eligible. A matched control population (n=1900) was acquired. Individual data on sick leave, compensations, comorbidity, and treatments assigned were assessed. Predictors were explored using multivariable logistic regression. </p> <p>Results: One year before surgery/index date, 88 % of cases were working compared to 91 % of controls. The proportion of controls working remained constant, while patients had a rapid increase in sick leave approximately six months prior to surgery. After one and two years respectively, 52 % and 63 % of the patients were working. Predictors for no-RTW after one year were previous sick leave (OR 0.92, 95 % CI 0.88-0.96, p <0.001), older age (OR 0.96, 95 % CI 0.94-0.99, p=0.005) and lower functional level (OR 0.64 95% CI, 0.45-0.91 p=0.01). Patients receiving adjuvant treatment were less likely to RTW within the first year. At two years, biopsy (as opposed to resection), female sex and co-morbidity were also unfavorable, while age and adjuvant treatment were no longer significant. </p> <p>Conclusions: Approximately half of the patients RTW within the first year. Lower functional status, previous sick leave, older age, and adjuvant treatment were risk factors for no-RTW at one year after surgery. Female sex, co-morbidity, and biopsy only were also unfavorable for RTW at two years.</p>
Artifacts for Incremental Search for Conflict and Unit Instances of Quantified Formulas with E-Matching
<p>This zip file contains the artifacts for the paper:</p> <p>J. Hoenicke and T. Schindler, <em>Incremental Search for Conflict and Unit Instances of Quantified Formulas with E-Matching, </em>VMCAI 2021, Springer</p> <p>The artifact is tested to work in the VMCAI 2021 virtual machine: <a href="https://doi.org/10.5281/zenodo.4017292">https://doi.org/10.5281/zenodo.4017292</a>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.