{"total":2,"items":[{"citing_arxiv_id":"2606.26563","ref_index":17,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"scBench-Long: Verifiable Benchmarking of Long-Horizon Single-Cell Biology","primary_cat":"q-bio.GN","submitted_at":"2026-06-25T03:21:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"scBench-Long is a benchmark with 21 evaluations where the strongest AI model-harness pair succeeds on 25.4% of long-horizon single-cell biology tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.24626","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data","primary_cat":"q-bio.GN","submitted_at":"2026-03-25T02:46:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[19] 6 3 3 0 0 0 6 16 5 4 Dai et al. [18] 2 3 2 0 1 1 3 8 1 4 Cheng et al. [17] 2 4 1 0 0 0 4 16 3 3 This Study 2 3 3 2 2 1 2 30 10 6 inherent to scRNA-seq experiments, arising from the limited amount of mRNA in individual cells [33], inefficiencies in reverse transcription [34], and stochastic vari- ability introduced during mRNA capture and amplification steps [16, 33, 34]. These technical limitations can lead to dropout events, where genes are observed as zero despite being expressed at low levels in the cell [16, 33-36]. However, not all zero counts arise from technical noise; zero counts in scRNA-seq data may also reflect a true biological absence of transcription, often referred to as biological zeros, which"}],"limit":50,"offset":0}