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scHG: A supercell framework with high-order graph learning enables scalable multi-omics analysis.

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Title: scHG: A supercell framework with high-order graph learning enables scalable multi-omics analysis.
Authors: Huang, Yixiang1 (AUTHOR), Gan, Yuan1 (AUTHOR), Gong, Xinqi1 (AUTHOR) xinqigong@ruc.edu.cn
Source: PLoS Computational Biology. 5/6/2026, Vol. 22 Issue 5, p1-36. 36p.
Database: Academic Search Ultimate
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  Data: scHG: A supercell framework with high-order graph learning enables scalable multi-omics analysis.
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Yixiang%22">Huang, Yixiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gan%2C+Yuan%22">Gan, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gong%2C+Xinqi%22">Gong, Xinqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xinqigong@ruc.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22PLoS+Computational+Biology%22">PLoS Computational Biology</searchLink>. 5/6/2026, Vol. 22 Issue 5, p1-36. 36p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=193529546
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1371/journal.pcbi.1013851
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      – Code: eng
        Text: English
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        PageCount: 36
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      – TitleFull: scHG: A supercell framework with high-order graph learning enables scalable multi-omics analysis.
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            NameFull: Huang, Yixiang
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            NameFull: Gan, Yuan
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            NameFull: Gong, Xinqi
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              Text: 5/6/2026
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              Y: 2026
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              Value: 22
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