{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T17:05:09Z","timestamp":1767200709364,"version":"3.48.0"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["2024ZB546"],"award-info":[{"award-number":["2024ZB546"]}]},{"name":"Big Data Computing Center of Southeast University"},{"name":"Scientific and Technological Innovation 2030 Major Projects","award":["2021ZD0204001"],"award-info":[{"award-number":["2021ZD0204001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>We present LazyNet, a compact one-step neural-ODE model for single-cell CRISPR activation\/interference (A\/I) that operates directly on two-snapshot (\u201cpre \u2192 post\u201d) measurements and yields parameters with clear mechanistic meaning. The core log\u2013linear\u2013exp residual block exactly represents multiplicative effects, so synergistic multi-locus responses appear as explicit components rather than opaque composites. On a 53k-cell \u00d7 18k-gene neuronal Perturb-seq matrix, a three-replica LazyNet ensemble trained under a matched 1 h budget achieved strong threshold-free ranking and competitive error (genome-wide r \u2248 0.67) while running on CPUs. For comparison, we instantiated transformer (scGPT-style) and state-space (RetNet\/CellFM-style) architectures from random initialization and trained them from scratch on the same dataset and within the same 1 h cap on a GPU platform, without any large-scale pretraining or external data. Under these strictly controlled, low-data conditions, LazyNet matched or exceeded their predictive performance while using far fewer parameters and resources. A T-cell screen included only for generalization showed the same ranking advantage under the identical evaluation pipeline. Beyond prediction, LazyNet exposes directed, local elasticities; averaging Jacobians across replicas produces a consensus interaction matrix from which compact subgraphs are extracted and evaluated at the module level. The resulting networks show coherent enrichment against authoritative resources (large-scale co-expression and curated functional associations) and concordance with orthogonal GPX4-knockout proteomes, recovering known ferroptosis regulators and nominating testable links in a lysosomal\u2013mitochondrial\u2013immune module. These results position LazyNet as a practical option for from-scratch, low-data CRISPR A\/I studies where large-scale pretraining of foundation models is not feasible.<\/jats:p>","DOI":"10.3390\/biology15010062","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T16:34:23Z","timestamp":1767198863000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LazyNet: Interpretable ODE Modeling of Sparse CRISPR Single-Cell Screens Reveals New Biological Insights"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4816-9270","authenticated-orcid":false,"given":"Ziyue","family":"Yi","sequence":"first","affiliation":[{"name":"School of Life Sciences and Technology, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Life Sciences and Technology, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanbo","family":"Ao","sequence":"additional","affiliation":[{"name":"School of Life Sciences and Technology, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.cell.2015.04.044","article-title":"Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells","volume":"161","author":"Klein","year":"2015","journal-title":"Cell"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly Parallel Genome-Wide Expression Profiling of Individual Cells Using Nanoliter Droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1126\/science.1069492","article-title":"Systems Biology: A Brief Overview","volume":"295","author":"Kitano","year":"2002","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1126\/science.1105809","article-title":"Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data","volume":"308","author":"Sachs","year":"2005","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1016\/j.cell.2016.11.039","article-title":"Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq","volume":"167","author":"Jaitin","year":"2016","journal-title":"Cell"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1038\/ng.3314","article-title":"The Support of Human Genetic Evidence for Approved Drug Indications","volume":"47","author":"Nelson","year":"2015","journal-title":"Nat. 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