{"ID":2828241,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15984","arxiv_id":"2512.15984","title":"Lifting Biomolecular Data Acquisition","abstract":"One strategy to scale up ML-driven science is to increase wet lab experiments' information density. We present a method based on a neural extension of compressed sensing to function space. We measure the activity of multiple different molecules simultaneously, rather than individually. Then, we deconvolute the molecule-activity map during model training. Co-design of wet lab experiments and learning algorithms provably leads to orders-of-magnitude gains in information density. We demonstrate on antibodies and cell therapies.","short_abstract":"One strategy to scale up ML-driven science is to increase wet lab experiments' information density. We present a method based on a neural extension of compressed sensing to function space. We measure the activity of multiple different molecules simultaneously, rather than individually. Then, we deconvolute the molecule...","url_abs":"https://arxiv.org/abs/2512.15984","url_pdf":"https://arxiv.org/pdf/2512.15984v1","authors":"[\"Eli N. Weinstein\",\"Andrei Slabodkin\",\"Mattia G. Gollub\",\"Kerry Dobbs\",\"Xiao-Bing Cui\",\"Fang Zhang\",\"Kristina Gurung\",\"Elizabeth B. Wood\"]","published":"2025-12-17T21:30:44Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"stat.ML\"]","methods":"[]","has_code":false}