{"id":"W4388961251","doi":"10.1101/2023.11.22.568376","title":"scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico Exploration","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University Health Centre; McGill University","funders":"National Key Research and Development Program of China; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Fonds de Recherche du Québec - Santé; Shandong University","keywords":"Data integration; Computer science; Omics; In silico; Modalities; Data science; Artificial intelligence; Data mining; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004618813,0.0006302616,0.000476786,0.0002157273,0.0003067484,0.0005415556,0.0003195417,0.000752907,7.782143e-7],"category_scores_gemma":[0.000134369,0.0006540618,0.000114371,0.0002504808,0.00020765,0.00005602132,0.0001986899,0.0004112334,0.000002096451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001646589,"about_ca_system_score_gemma":0.0005763253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004702923,"about_ca_topic_score_gemma":0.0005872697,"domain_scores_codex":[0.9971928,0.00009584543,0.0006395845,0.00134746,0.0002193029,0.0005050008],"domain_scores_gemma":[0.9978622,0.00002639498,0.0003377681,0.0006986898,0.0008978813,0.0001770234],"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":[0.0002037939,0.0002246061,0.005422485,0.0002060385,0.00006519438,0.000004709948,0.0001085829,0.07239982,0.9212601,0.00007043442,0.00002910284,0.000005143236],"study_design_scores_gemma":[0.001451847,0.0001235336,0.001375636,0.00008480252,0.00004511523,2.721124e-8,0.00001604774,0.3634387,0.6328164,0.000005414774,0.00004698696,0.0005955554],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5608096,0.0003398387,0.4374479,0.00005866656,0.0002818984,0.0007893817,0.0002089062,0.00006312083,6.531375e-7],"genre_scores_gemma":[0.945491,0.0003280742,0.0525528,0.0001782262,0.000513619,0.0006444314,0.00004840203,0.0002098509,0.0000335857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3848951,"threshold_uncertainty_score":0.9995911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05709065548695007,"score_gpt":0.260405609179331,"score_spread":0.203314953692381,"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."}}