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arXiv Statistics ML By Jeremy Levy, Ariel Larey, Yury Nahshan, Raizy Kellerman, Elay Dahan, Amit Bleiweiss, Guy Leib, Omri Nayshool, Dan Ofer, Tal Zinger, Dan Dominissini, Gideon Rechavi, Marissa Wirth, Simon Lee, Dung Hoang, Noam D. Beckmann, Shane O'Connell, Nicole Bussola, Alexander W. Charney, Yoli Shavit, Nati Daniel

VANDAM: Viewing a nucleotide sequence with DNA molecular priors

Read the original on arXiv Statistics ML →

VANDAM is a framework that augments Genomic Foundation Models by incorporating DNA molecular priors into self‑supervised training. It predicts regional molecular properties from pooled representations and, when functional labels are available, injects local features at the input. The approach consistently improves downstream performance across multiple architecture families and genomic tasks, and probing experiments show that the priors generalize to unseen molecular properties.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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