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598 results for “classifier”
16S V4-V4 taxonomy classifier
<p>16S metabarcoding databases and naive-bayes classifier specific to the V4-V5 region. Built from the <a href="https://www.arb-silva.de/documentation/release-138/">Silva 138.1 SSU Ref NR 99</a> database using Qiime2 (version 2021.2).</p> <p>Primers used:</p> <p>EMP 16S 515f: GTGYCAGCMGCCGCGGTAA</p> <p>EMP 16S 926r: CCGYCAATTYMTTTRAGTTT</p> <table> <caption>File description</caption> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>silva-138-99-seqs.qza</td> <td>Full length Silva 138.1 SSU 99 sequences</td> </tr> <tr> <td>silva-138-99-tax.qza</td> <td>Taxa for full length Silva 138.1 SSU 99 database</td> </tr> <tr> <td>refseqs_V4-V5.qza</td> <td>Sequences for 16S V4-V5 (primers 515f, 926r), extracted from Silva 138.1 SSU 99, generated by qiime2-2021.2 (forward compatible)</td> </tr> <tr> <td>classifier_V4-V5.qza</td> <td>Unweighted (uniform) naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, generated by qiime2-2021.2 </td> </tr> </tbody> </table> </th> <th scope="col"> </th> </tr> </thead> </table>
Study on the Effectiveness and Safety of the Combination of the Two Drugs Regorafenib and Nivolumab in Patients With Colorectal Cancer (Cancer of the Colon or Rectum Classified as Proficient Mismatch
ClinicalTrials.gov study NCT04126733. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Testing of Drugs Erlotinib and Docetaxel in Lung Cancer Patients Classified Regarding Their Outlook Using VeriStrat®.
ClinicalTrials.gov study NCT01652469. IPD Sharing: NO. Countries: 12. Publications: 7.
Neighborhood benthic configuration reveals hidden social diversity: classified benthic data
Open the record for dataset details and reuse information.
Ground truth data used to train the synapse classifier used in Lillvis et al., 2022 for ExLLSM circuit reconstruction
Open the record for dataset details and reuse information.
Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining
Open the record for dataset details and reuse information.
From buzzes to bytes: A systematic review of automated bioacoustics models used to detect, classify, and monitor insects
Open the record for dataset details and reuse information.
Supporting codes, data, and outputs to identify a type of lithofacies from rock-core digital photographs and to classify the lithofacies from well-log data
<p>This repository contains the codes and the data used in the study “Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information from Well-log Data and Rock-core Digital Photographs”. The codes are used to identify the lithofacies type from rock-core digital photographs (clustering) and to predict the lithofacies from geophysical well-log data (classification). The lithofacies identification process, including feature extraction and clustering, was encoded by MATLAB, and the classification model was encoded by TensorFlow to construct a neural network-based classification model. </p>
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively.
Box 4 in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Box 4 | Classifying SARS-CoV-2 The species demarcation threshold (also known as demarcation limit) in the family Coronaviridae is defined by viruses whose PPD(s) may cross the inter-species demarcation PPD threshold (threshold 'violators'). Due to their minute share of ~10–4 of the to- tal number of all intra- and inter-species PPDs, these violators may not even be visually recognized in a conventional diagonal plot clus- tering viruses on a species basis (panel a of the figure in Box 4). Furthermore, they do not involve any virus of the species Severe acute respiratory syndrome-related coronavirus, as is evident from the analysis of maximal intraspecies PPDs of 2,505 viruses of all 49 coronavirus species (of which 39 are established and 10 are pending or tentative) (panel b of the figure in Box 4) and PDs of 256 viruses of this species (panel c of the figure in Box 4). Thus, the genomic variation of the known viruses of the species Severe acute respiratory syndrome-related coronavirus is smaller compared to that of other comparably well-sampled species—for example, those prototyped by MERS-CoV, human coronavirus OC43 (HCoV-OC43) and in- fectious bronchitis virus (IBV) (panel b of the figure in Box 4)—and this species is well separated from other known coronavirus species in the sequence space. Both of these characteristics facilitate the un- ambiguous assignment of SARS-CoV-2 to this species. Intra-species PDs of SARS-CoV-2 belong to the top 25% of this species and also include the largest PD between SARS-CoV-2 and an African bat virus isolate (SARSr-CoV_BtKY72)56 (panel c of the figure in Box 4), representing two basal lineages within the species Severe acute respiratory syndrome-related coronavirus that constitute very few known viruses (Fig. 2b,c). These relationships stand in contrast to the shallow branching of the most populous lineage of this species, which includes all the human SARS-CoV isolates collected during the 2002–2003 outbreak and the closely related bat viruses of Asian origin identified in the search for the potential zoonotic source of that epidemic57. This clade structure is susceptible to homologous recombination, which is common in this species44,58,59; to formalize clade definition, it must be revisited after the sampling of viruses representing the deep branches has improved sufficiently. The current sampling defines a very small median PD for human SARS-CoVs, which is approximately 15 times smaller than the median PD determined for SARS-CoV-2 (0.16% versus 2.6%; panel c of the figure in Box 4). This small median PD of human SARS-CoVs also dominates the species- wide PD distribution (0.25%; panel c of the figure in Box 4). Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
Fig. 2 in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Fig. 2 | Phylogeny of coronaviruses. a, Concatenated multiple sequence alignments (MSAs) of the protein domain combination44 used for phylogenetic and DEmARC analyses of the family Coronaviridae. Shown are the locations of the replicative domains conserved in the ordert Nidovirales in relation to several other ORF1a/b-encoded domains and other major ORFs in the SARS-CoV genome. 5d, 5 domains: nsp5A-3CLpro, two beta-barrel domains of the 3C-like protease; nsp12-NiRAN, nidovirus RdRp-associated nucleotidyltransferase; nsp12-RdRp, RNA-dependent RNA polymerase; nsp13-HEL1 core, superfamily 1 helicase with upstream Zn-binding domain (nsp13-ZBD); nt, nucleotide. b, The maximum-likelihood tree of SARS-CoV was reconstructed by IQ-TREE v.1.6.1 (ref. 45) using 83 sequences with the best fitting evolutionary model. Subsequently, the tree was purged from the most similar sequences and midpoint-rooted. Branch support was estimated using the Shimodaira–Hasegawa (SH)-like approximate likelihood ratio test with 1,000 replicates. GenBank IDs for all viruses except four are shown; SARS-CoV, AY274119.3; SARS-CoV-2, MN908947.3; SARSr-CoV_BtKY72, KY352407.1; SARS-CoV_PC4-227, AY613950.1. c, Shown is an IQ-TREE maximum-likelihood tree of single virus representatives of thirteen species and five representatives of the species Severe acute respiratory syndrome-related coronavirus of the genus Betacoronavirus. The tree is rooted with HCoV-NL63 and HCoV-229E, representing two species of the genus Alphacoronavirus. Purple text highlights zoonotic viruses with varying pathogenicity in humans; orange text highlights common respiratory viruses that circulate in humans. Asterisks indicate two coronavirus species whose demarcations and names are pending approval from the ICTV and, thus, these names are not italicized.
Crowd-Annotation Results: Identifying and Classifying User Requirements in Online Feedback
<p>Results from the Figure Eight experiment conducted as part of the paper "Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing" published at REFSQ 2020.</p>
CLDF dataset derived from Tang and Her's "Quantitative typological data on classifiers and plural markers" from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Tang, Marc and One-Soon Her. 2019. Insights on the Greenberg-Sanches-Slobin Generalization: Quantitative typological data on classifiers and plural markers. Folia Linguistica, 53(2): 297-331. https://doi.org/10.1515/flin-2019-2013</p> </blockquote>
Classify on the Clock (CloCk) - An Entry Level Image Data Set for Neural Networks
<p><strong>General information</strong></p> <p>This data set contains synthetic images of an analog clock. Each time point from 00:00:00 - 11:59:59 is included as a separate image. We provide three different versions of each image with an increasing number of additional information:</p> <ul> <li><em>Sparse</em>: The image includes just the hour, minute and second hand</li> <li><em>Reduced</em>: Additional markings around the clock</li> <li><em>Full</em>: Additional written numbers</li> </ul> <p>The hands differ in size and color:</p> <ul> <li><em>Hour</em>: Black, short, wide</li> <li><em>Minute</em>: Blue, long, medium</li> <li><em>Second</em>: Red, long, slim</li> </ul> <p>We provide the following files in this data repository:</p> <ul> <li>RGB images with a size of 512x512 for all three versions</li> <li>A suggested training, validation and test split</li> <li>The creation script as Python file</li> </ul> <p>The script can easily be modified to create images with a different size and color.</p> <p> </p> <p><strong>Clock System</strong></p> <p>The movements of the hands follow a linear relationship. Their angles can be calculated by the forward system:</p> <p><span class="math-tex">\( \begin{bmatrix} 6^\circ & 0 & 0 \\ 0.1^\circ & 6^\circ & 0 \\ 0 & 0.5^\circ & 30^\circ \end{bmatrix} \begin{pmatrix} n_{\text{sec}} \\ n_{\text{min}} \\ n_{\text{hour}} \end{pmatrix} = \begin{pmatrix} \alpha_{\text{sec}}\\ \alpha_{\text{min}} \\ \alpha_{\text{hour}} \end{pmatrix}.\)</span></p> <p>One can also introduce rotated versions of the images. The system becomes non-linear in this case:</p> <p><span class="math-tex">\(\operatorname{mod}\left( \begin{bmatrix} 6^\circ & 0 & 0 \\ 0.1^\circ & 6^\circ & 0 \\ 0 & 0.5^\circ & 30^\circ \end{bmatrix} \begin{pmatrix} n_{\text{sec}} \\ n_{\text{min}} \\ n_{\text{hour}} \end{pmatrix} + \omega, \, 360^\circ \right) = \begin{pmatrix} \alpha_{\text{sec}}\\ \alpha_{\text{min}} \\ \alpha_{\text{hour}} \end{pmatrix}.\)</span></p> <p> </p> <p><strong>Use Cases</strong></p> <p>This data set was originally designed for basic research on Capsule Networks. Use cases are:</p> <ul> <li>Evalutation of classification and regression performance</li> <li>Influence of image transformations, e.g. rotations</li> <li>Detection of the hierarchy & relationship of image parts</li> <li>Concealment of objects in the image</li> <li>Interpretability of the learned model</li> <li>Solving a discrete inverse problem (<em>sparse</em> version)</li> <li>...</li> </ul>
Recalibrating classifiers for interpretable abusive content detection
<p>Dataset and code for the paper, 'Recalibrating classifiers for interpretable abusive content detection' by Vidgen et al. (2020) -- to appear at the <a href="https://sites.google.com/site/nlpandcss/">NLP + CSS workshop</a> at <a href="https://2020.emnlp.org">EMNLP 2020</a>.</p> <p>We provide:</p> <ol> <li>1,000 annotated tweets, sampled using the Davidson classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election</li> <li>1,000 annotated tweets, sampled using the Perspective classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election</li> <li>Code for recalibration in R and STAN.</li> <li>Annotation guidelines for both datasets.</li> </ol> <p> </p> <p><strong>Paper abstract</strong></p> <p>We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the 'raw' scores are used) align poorly with human evaluations. This limits their use to understand the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK's 2017 general election.<br> A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.</p> <p> </p> <p> </p>
Residential Building Occupancy Class Image Classifier
<p>0 : RES1 (single-family)</p> <p>1 : RES3 (multi-family)</p>
The ALeRCE Light Curve Classifier: labeled set, features, and classifications
<p>Labeled set, features, and classifications of the ZTF alert stream (up to 2020/06/09) presented in the article "Alert Classification for the ALeRCE Broker System: The Light Curve Classifier", Sánchez-Sáez et al., accepted for publication in the Astronomical Journal.</p> <p>The labeled set is used by the ALeRCE broker (<a href="http://alerce.science">alerce.science</a>) to provide updated classifications for sources with new ZTF alerts every day. To obtain updated classifications visit <a href="http://alerce.online">alerce.online</a>. In addition, more examples and instructions on how to use the ALeRCE database and classifications can be found on the <a href="http://alerce.science">alerce.science</a> website. </p> <p> </p> <p>File description:</p> <ul> <li>"labeled_set_lc_classifier_SanchezSaez_2020.csv" contains the labeled set described in Section 2.2 of the paper.</li> <li>"features_for_lc_classifier_20200609.csv" contains the features computed for the unlabeled ZTF set (up to 2020/06/09). The features are described in Section 3, and Appendix A of the paper.</li> <li>"ALeRCE_lc_classifier_outputs_ZTF_unlabeled_set_20200609.csv" contains the probabilities obtained for each level of the model, as well as the final predictions and probabilities (up to 2020/06/09). For more details about the model see Sections 4.1 and 5.3.</li> </ul> <p> </p> <p> </p>
Data from: The preference-performance relationship as a means of classifying parasitoids according to their specialization degree
Host range in parasitoids could be described by the preference-performance hypothesis (PPH) where preference is defined as host acceptance and performance is defined as the sum of all species on which parasitoid offspring can complete their life cycle. The PPH predicts that highly suitable hosts will be preferred by ovipositing females. However, generalist parasitoids may not conform to this hypothesis if they attack a large range of hosts of varying suitability. Under laboratory conditions, we tested the PPH relationship of three aphid parasitoids currently considered as generalist species (Aphelinus abdominalis, Aphidius ervi, Diaeretiella rapae). As expected, the three parasitoids species showed low selectivity i.e. females stung all aphid species encountered (at least in some extent). However, depending on the parasitoid species, only 42-58% of aphid species enabled producing parasitoid offspring. We did not find a correlation between the extent of preference and the performance of three generalist aphid parasitoids. For A. ervi, host phylogeny is also important as females showed higher attack and developmental rates on hosts closely related to the most suitable one. In addition, traits such as (i) the presence of protective secondary endosymbionts, e.g., Hamiltonella defensa detected in Aphis fabae and Metopolophium dirhodum, and (ii) the sequestration of plant toxins as defense mechanism against parasitism e.g., in Aphis nerii and Brevicoryne brassicae, were likely at play to some extent in narrowing parasitoid host range. The lack of PPH relationship involved a low selectivity leading to a high adaptability, as well as selection pressure; the combination of which enabled the production of offspring in a new host species or a new environment. Testing for PPH relationships in parasitoids may provide useful cues to classify parasitoids in terms of specialization degree.
Data from: Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time‐consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation–environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation–environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation–environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well‐structured vegetation–environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation–environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions.
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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.