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Bioinformatics, Machine Learning and Functional Validation Reveal Mitophagy-Related FABP5 and HMOX1 as Diagnostic and Therapeutic Targets in Polycystic Ovary Syndrome

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Τίτλος: Bioinformatics, Machine Learning and Functional Validation Reveal Mitophagy-Related FABP5 and HMOX1 as Diagnostic and Therapeutic Targets in Polycystic Ovary Syndrome
Συγγραφείς: Lian, AiqinAff2, Li, YuxinAff1, He, WeijunAff1, Luo, XinjianAff1, Jin, LongAff1, Li, ChaohuiAff1, IDs4303202602156x_cor1
Πηγή: Reproductive Sciences. 33(10):2018-2036
Βάση Δεδομένων: Springer Nature Journals
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  – Url: https://dx.doi.org/doi:10.1007/s43032-026-02156-x
    Name: EDS - Springer Nature Journals (s7799221)
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  Data: Bioinformatics, Machine Learning and Functional Validation Reveal Mitophagy-Related FABP5 and HMOX1 as Diagnostic and Therapeutic Targets in Polycystic Ovary Syndrome
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  Data: <i>Reproductive Sciences</i>. 33(10):2018-2036
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        Value: 10.1007/s43032-026-02156-x
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      – Text: English
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        PageCount: 19
        StartPage: 2018
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      – TitleFull: Bioinformatics, Machine Learning and Functional Validation Reveal Mitophagy-Related FABP5 and HMOX1 as Diagnostic and Therapeutic Targets in Polycystic Ovary Syndrome
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            – D: 01
              M: 10
              Type: published
              Y: 2026
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