{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T07:56:38Z","timestamp":1781337398274,"version":"3.54.1"},"reference-count":48,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    DNA methylation (DNAm) is a key epigenetic modification, and datasets capturing DNAm are typically high-dimensional. Although dimension reduction (DR) techniques are commonly applied, it remains unclear how different DR methods perform specifically on the DNAm dataset. In this study, we aim to evaluate the performance of several DR techniques to determine their performance for reducing the dimensionality of DNAm datasets. We leveraged the DNAm dataset from the STRiDE (STratification of Risk of Diabetes in Early pregnancy) prospective study, which consists of 8,62,927 CpG sites each from 258 pregnant women.\u00a0 Women were categorized as Normal (\n                    <jats:italic>n<\/jats:italic>\n                    = 146); and GDM (\n                    <jats:italic>n<\/jats:italic>\n                    = 112). Epigenome-wide DNA methylation profiles from peripheral blood were quantified using an Infinium Methylation EPIC array. All stated DR techniques were performed, and the retained amount of information, local neighborhood preservation criteria, and global structure-holding approaches were assessed using various statistical measures and compared. Across Shannon entropy, local\u2010neighborhood metrics (K\u00f6nig\u2019s measure, Spearman\u2019s \u03c1, trustworthiness\/continuity) and global\u2010structure metrics (Kruskal stress, Sammon\u2019s stress, residual variance), MDS and PCA consistently achieved the best performance. PLS-DA trailed closely, while ISOMAP showed moderate results and UMAP performed worst, exhibiting higher entropy, lower correlation preservation, and greater distortion of both local and global structures.\n                  <\/jats:p>","DOI":"10.1515\/ijb-2025-0071","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T06:59:08Z","timestamp":1771397948000},"page":"127-142","source":"Crossref","is-referenced-by-count":0,"title":["Performance evaluation of\u00a0dimensionality reduction techniques on\u00a0high-dimensional DNA methylation data"],"prefix":"10.1515","volume":"22","author":[{"given":"Kuldeep Kumar","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Biostatistics , 29148 National Institute of Mental Health & Neuro Sciences (NIMHANS) , Bangalore , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuppan","family":"Gokulakrishnan","sequence":"additional","affiliation":[{"name":"Department of Neurochemistry , National Institute of Mental Health & Neuro Sciences (NIMHANS) , Bangalore , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binu","family":"V S","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , 29148 National Institute of Mental Health & Neuro Sciences (NIMHANS) , Bangalore , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binukumar","family":"Bhaskarapillai","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , 29148 National Institute of Mental Health & Neuro Sciences (NIMHANS) , Bangalore , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chinnasamy","family":"Thirumoorthy","sequence":"additional","affiliation":[{"name":"Department of Neurochemistry , National Institute of Mental Health & Neuro Sciences (NIMHANS) , Bangalore , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ponnusamy","family":"Saravanan","sequence":"additional","affiliation":[{"name":"Diabetes , Endocrinology & Metabolism , George Eliot Hospital NHS Trust , Nueaton , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"2026061307200642560_j_ijb-2025-0071_ref_001","doi-asserted-by":"crossref","unstructured":"Young, GA. 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