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Sparse Bernoulli mixture modeling with negative-unlabeled data: an approach to identify and characterize long COVID.

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Title: Sparse Bernoulli mixture modeling with negative-unlabeled data: an approach to identify and characterize long COVID.
Authors: Cao, Tingyi1, tingyi_cao@hsph.harvard.edu, Reeder, Harrison T2,3, Foulkes, Andrea S1,2,3
Source: Biometrics; Mar2025, Vol. 81 Issue 1, p1-11, 11p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 185489163
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PubType: Academic Journal
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  Data: Sparse Bernoulli mixture modeling with negative-unlabeled data: an approach to identify and characterize long COVID.
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  Data: <searchLink fieldCode="AU" term="%22Cao%2C+Tingyi%22">Cao, Tingyi</searchLink><relatesTo>1</relatesTo>, <i>tingyi_cao@hsph.harvard.edu</i><br /><searchLink fieldCode="AU" term="%22Reeder%2C+Harrison+T%22">Reeder, Harrison T</searchLink><relatesTo>2,3</relatesTo><br /><searchLink fieldCode="AU" term="%22Foulkes%2C+Andrea+S%22">Foulkes, Andrea S</searchLink><relatesTo>1,2,3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Biometrics%22">Biometrics</searchLink>; Mar2025, Vol. 81 Issue 1, p1-11, 11p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=185489163
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    Identifiers:
      – Type: doi
        Value: 10.1093/biomtc/ujaf021
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1
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      – TitleFull: Sparse Bernoulli mixture modeling with negative-unlabeled data: an approach to identify and characterize long COVID.
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            NameFull: Cao, Tingyi
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            NameFull: Reeder, Harrison T
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            NameFull: Foulkes, Andrea S
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            – D: 01
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              Text: Mar2025
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              Y: 2025
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