{"id":"W4403029705","doi":"10.1101/2024.09.30.615775","title":"SC-MAMBA2: Leveraging State-Space Models for Efficient Single-Cell Ultra-Long Transcriptome Modeling","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Transcriptome; Computer science; Space (punctuation); State (computer science); State space; Biology; Algorithm; Mathematics; Operating system; Genetics; Statistics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007120033,0.001103602,0.0008133078,0.0003293842,0.0002787796,0.0005029818,0.0008413632,0.0009389281,0.000006438561],"category_scores_gemma":[0.00006529301,0.001229999,0.0006663598,0.000326624,0.0001360751,0.00001657685,0.0003063738,0.0009693779,0.00001748091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002651575,"about_ca_system_score_gemma":0.0007431763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007828062,"about_ca_topic_score_gemma":0.000008350406,"domain_scores_codex":[0.9951205,0.0001189021,0.0009056524,0.002178996,0.0004950461,0.001180896],"domain_scores_gemma":[0.9972326,0.00003925705,0.0002587858,0.001416585,0.0006298892,0.0004229294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001455497,0.0002732771,0.00006125282,0.001059416,0.0001872203,0.00001952156,0.00005364348,0.26688,0.7311552,0.00006449447,0.00009690094,0.000003514001],"study_design_scores_gemma":[0.0009495466,0.0001617051,0.00002297496,0.0005009694,0.0002489855,7.08843e-8,0.000008517702,0.2728433,0.7232088,0.00002496971,0.0007651395,0.00126503],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6622391,0.00586334,0.3279145,0.0001306913,0.001902122,0.001103572,0.0005558796,0.0002567643,0.00003404858],"genre_scores_gemma":[0.9903791,0.00043943,0.007324319,0.0002255892,0.0007718673,0.0003178948,0.000009761355,0.0004759994,0.0000560302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3281401,"threshold_uncertainty_score":0.999015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0230219584314686,"score_gpt":0.2169064728131853,"score_spread":0.1938845143817167,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}