{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:18:29Z","timestamp":1792790309788,"version":"4.5.0"},"posted":{"date-parts":[[2026,9,15]]},"group-title":"Bioinformatics","reference-count":52,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Deutsche Forschungsgemeinschaft","award":["KU 4686\/1-1"],"award-info":[{"award-number":["KU 4686\/1-1"]}],"id":[{"id":"https:\/\/ror.org\/018mejw64","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Deutsche Forschungsgemeinschaft","award":["KU 4686\/2-1"],"award-info":[{"award-number":["KU 4686\/2-1"]}],"id":[{"id":"https:\/\/ror.org\/018mejw64","id-type":"ROR","asserted-by":"crossref"}]},{"name":"HERCULES Exposome Research Center","award":["NIEHS P30ES019776"],"award-info":[{"award-number":["NIEHS P30ES019776"]}]},{"name":"Goizueta Alzheimer\u2019s Disease Research Center","award":["NIA P30 AG055611"],"award-info":[{"award-number":["NIA P30 AG055611"]}]},{"name":"Goizueta Alzheimer\u2019s Disease Research Center","award":["NIH Grant R01AG087250"],"award-info":[{"award-number":["NIH Grant R01AG087250"]}]},{"name":"Rollins School of Public Health Dean\u2019s Pilot and Innovation Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,15]]},"abstract":"<jats:p>\n                  Data availability is critical for understanding complex disease pathways and developing robust predictive models. Although high-throughput omics technologies have improved insight into disease mechanisms, data acquisition from inaccessible tissues such as the central nervous system remains a major limitation, causing small sample sizes and complicating early prediction of neurodegenerative disorders such as Alzheimer\u2019s and Parkinson\u2019s diseases. Generative modeling has emerged as a powerful approach for synthesizing data to support downstream clustering and prediction with small sample size, but existing methods rarely handle high-dimensional tabular omics data effectively. Adversarial random forests (ARFs) provide a well-performing framework for tabular data generation but are not designed for high-dimensional settings. To address this limitation, we introduce high-dimensional ARF (\n                  <jats:italic>h<\/jats:italic>\n                  -ARF), an extension of ARF optimized for integrated clinical and high-dimensional omics data. Using benchmarks across nine datasets and eight performance metrics, we show that\n                  <jats:italic>h<\/jats:italic>\n                  -ARF better preserves both feature distributions, and downstream clustering and prediction utilities compared with ARFs. The method is implemented in the opensource R package harf, available on CRAN.\n                <\/jats:p>","DOI":"10.64898\/2026.09.09.750490","type":"posted-content","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:20:16Z","timestamp":1789536016000},"source":"Crossref","is-referenced-by-count":1,"title":["Adversarial random forests for omics synthesis"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2166-6955","authenticated-orcid":false,"given":"Cesaire J. 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