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3 results for “multi-label classifier”
Dataset: A multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern
<p>NORMAN Suspect List Exchange was used for the generation of the dataset. Datasets with clear label (LC or GC) were used. More specifically, we used S3 NORMANCT15, which contains a list of compounds that were detected in surface water from the Danube River in a pan-European collaborative trial employing both GC-HRMS and LC-HRMS. Moreover, the GC and LC target list were used by the following two institutes: National and Kapodistrian University of Athens (NKUA) and Helmholtz Centre for Environmental Research (UFZ). S21 UATHTARGETS is the LC target list of NKUA, S65 UATHTARGETSGC is the GC target list of NKUA and S53 UFZWANATARG contains the LC and GC target list of UFZ. Finally, two GC target lists (S51 WRIGCHRMS and S70 EISUSGCEIMS) were used. These lists contain GC substance lists and were provided by two Slovak institutes, the Water Research Institute (WRI) and Environmental Institute. The aforementioned compound lists were merged together to form a labelled dataset. The SMILES were used to calculate 1446 molecular descriptors. 1446 descriptors were produced by PaDEL-descriptor, logP was produced by JRgui and boiling point by USEPA ECOSAR.</p> <p>The dataset is used in the publication:</p> <p>"A multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern" authored by</p> <p>Nikiforos Alygizakis, Vasileios Konstantakos, Grigoris Bouziotopoulos , Evangelos Kormentzas, Jaroslav Slobodnik and Nikolaos S. Thomaidis</p> <p>Github repository: https://github.com/nalygizakis/LCvsGC</p>
Supplementary datasets for the paper of "Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization"
<p>There are two eCLIP datasets (cell lines of K562 and HepG2). The eCLIP datasets were then divided into five categories for each cell line: low, medium 1, medium 2, high 1 and high 2 with peaks of >1,000 but <2,000, >2,000 but <4,000, >4,000 but <7,000, >7,000 but <10,000 and >10,000, respectively. </p>
DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes
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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.