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56 results for “False positives”
True positive rate and false positive rate used to generate S2_Fig1B and S2_Table3
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's F, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during feedback session, S2_Fig1B and S2_Table3, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig2A and S2_Table4
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's G, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during training session, S2_Fig2A and S2_Table4, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig2B and S2_Table5
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's G, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during feedback session, S2_Fig2B and S2_Table5, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig1A and S2_Table2
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's F, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during training session, S2_Fig1A and S2_Table2, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig4A and S2_Table8
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's W, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during training session, S2_Fig4A and S2_Table8, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig3A and S2_Table6
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's B, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during training session, S2_Fig3A and S2_Table6, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig2A and S2_Table4
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's G, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during training session, S2_Fig2A and S2_Table4, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig4B and S2_Table9
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's W, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during training session, S2_Fig4B and S2_Table9, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig2B and S2_Table5
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's G, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during feedback session, S2_Fig2B and S2_Table5, respectively.</p>
True positive rate and false positive rate used to generate S2_Fig1B and S2_Table3
<p>In the attached file, first column corresponds to true positive (TP), second column corresponds to false positive (FP), third column corresponds to false negative (FN), fourth column corresponds to true negative (TN), fifth column corresponds to true positive rate (TPR) and sixth column corresponds to false positive rate (FPR), while each row corresponds to the session number. The data was used to generate patient's F, "Receiver operating characteristic (ROC) curve of the binary support vector machine (SVM) classifier" and "Contingency table" during feedback session, S2_Fig1B and S2_Table3, respectively.</p>
Cystic Fibrosis Diagnosis in Newborns with Machine Learning]{Newborn Cystic Fibrosis Diagnosis Made Accurate and Efficient with Machine Learning to Reduce False Positives in IRT-Trypsinogen Immunoreactive Screening Program
<p>Datasets to training models in this article and generator code.</p>
Correspondence on Li Yumei et al.: Exaggerated false positives by popular differential expression methods when analyzing human population samples.
<p>Scripts for manuscript</p>
Factors Associated With PSA False Negative and False Positive Results and the Impact on Patient's Health.
ClinicalTrials.gov study NCT03978299. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Detection of False Positives From First-trimester Preeclampsia Screening (StopPRE) at the Second-trimester of Pregnancy
ClinicalTrials.gov study NCT03741179. IPD Sharing: Not stated. Countries: 1. Publications: 1.
False Positive Results in Newborn Hearing Screening
ClinicalTrials.gov study NCT00753194. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Occupancy in dynamic systems: accounting for multiple scales and false positives using environmental DNA to inform monitoring
Open the record for dataset details and reuse information.
Data from: Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys
Open the record for dataset details and reuse information.
False-Positive Psychology in .txt format
<p>Data for the paper http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1850704</p> <p> </p>
IGV screenshot for manual validation of 60 reported false positive small CNVs in SurVIndel2
Open the record for dataset details and reuse information.
Supplementary plots for "Ephemeris Matching Reveals False Positive Validated and Candidate Planets From the K2 Mission"
<p>Supplementary plots for false positive matches as described in section 3 of the paper.</p>
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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.