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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.18498 |
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| _version_ | 1866908788867465216 |
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| author | Liu, Mingyan Huang, Min |
| author_facet | Liu, Mingyan Huang, Min |
| contents | Major Depressive Disorder (MDD) is a clinically heterogeneous syndrome with diverse etiological pathways. Traditional Epigenome-Wide Association Studies (EWAS) have successfully identified risk loci based on differential methylation magnitude. As a complementary perspective, effect-size-based ranking alone may not fully capture regulatory nodes that exhibit modest methylation changes but occupy critical upstream positions in biological networks. Here, we report findings and hypotheses from a two-tier computational analysis of DNA methylation data (GSE198904; \(n=206\) ), combining conventional statistical approaches with machine learning-assisted regulatory inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18498 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Regulatory Hub Discovery in MDD Methylome: Hypotheses for Molecular Subtypes via Computational Analysis Liu, Mingyan Huang, Min Computational Engineering, Finance, and Science Major Depressive Disorder (MDD) is a clinically heterogeneous syndrome with diverse etiological pathways. Traditional Epigenome-Wide Association Studies (EWAS) have successfully identified risk loci based on differential methylation magnitude. As a complementary perspective, effect-size-based ranking alone may not fully capture regulatory nodes that exhibit modest methylation changes but occupy critical upstream positions in biological networks. Here, we report findings and hypotheses from a two-tier computational analysis of DNA methylation data (GSE198904; \(n=206\) ), combining conventional statistical approaches with machine learning-assisted regulatory inference. |
| title | Regulatory Hub Discovery in MDD Methylome: Hypotheses for Molecular Subtypes via Computational Analysis |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2601.18498 |